IL178769A - System for performing virtual colonoscopy - Google Patents

Description

fittHtt n>aip iii ip ywi iit?w -nmvtt SYSTEM FOR PERFORMING VIRTUAL COLONOSCOPY This application is a divisional application of IL 145516.

SPECmCATION TECHNICAL FIELD The present invention relates to a system r performing virtual colonoscopy.

BACKGROUND OF THE INVENTION Colon cancer continues to be a major cause of death throughout the world. Early detection of cancerous growths, which in the human colon initially manifest themselves as polyps, can greatly improve a patient's chance of recovery. Presently, there are two conventional ways of detecting polyps or other masses in the colon of a patienL The first method is a colonoscopy procedure, which uses a flexible fiber-optic tube called a colonoscope to visually examine the colon by way of physical rectal entry with the scope The doctor can manipulate the tube to search for any abnormal growths in the colon. The colonoscopy, although reliable, is bom relatively costly in money and time, and is an invasive, uncomfortable painful procedure for the patient The second detection technique is the use of a barium enema and two- dimensional X-ray imaging of the colon. The barium enema is used to coat the colon with barium, and a two-dimensional X-ray image is taken to capture an image of the colon. However, barium enemas may not always provide a view of the entire colon, require extensive pretreatment and patient manipulation, is often operator-dependent when performing the operation, exposes the patient to excessive radiation and can be less sensitive than a colonoscopy. Due to deficiencies in the conventional practices described above, a more reliable, less intrusive and less expensive way to check the colon for polyps is desirable. A method to examine other human organs, such as the lungs, for masses in a reliable, cost effective -way and with less patient discomfort is also desirable.

Two-dimensional ("2D") visualization of human organs employing currently available medical imaging devices, such as computed tomography and RI (magnetic resonance imaging), has been widely used for patient diagnosis. Three-dimensional images can be formed by stacking and interpolating between two-dimensional pictures produced from the scantling machines. Imaging an organ and visualizing its volume in three-dimensional space would be beneficial due to its lack of physical intrusion and the ease of data manipulation. However, the exploration of the three-dimensional volume image must be properly performed in order to fully exploit the advantages of virtually viewing an organ from the inside.

When viewing the three dimensional ("3D") volume virtual image of , an environment, a functional model must be used to explore the virtual space. One possible model is a virtual camera which can be used as a point of reference for the viewer to explore the virtual space. Camera control in the context of navigation within a general 3D virtual environment has been previously studied. There are two conventional types of camera control offered for navigation of virtual space. The Sxst gives the operator full control of the camera which allows the operator to manipulate the camera in different positions and orientations to achieve the view desired. The operator will in effect pilot the camera. This allows the operator to explore a particular section of interest while ignoring other sections. However, complete control of a camera in a large domain would be tedious and tiring, and an operator might not view all the important features between the start and finishing point of the exploration. The camera could also easily get "lost" in remote areas or be "crashed" into one of the walls by an inattentive operator or by numerous unexpected obstacles.

The second technique of camera control is a planned navigation method, which assigns the camera a predetermined path to take and which cannot be changed by the operator. This is akin to having an engaged "autopilot''. This allows the operator to concentrate on the virtual space being viewed, and not have to worry about steering into walls of the environment being examined, However, this second technique docs not give the viewer the flexibility to alter the course or investigate an interesting area viewed along the flight path.

It would be desirable to use a combination of the two navigation techniques described above to realize the advantages of both techniques while minimising their respective drawbacks. It would be desirable to apply a flexible navigation technique to the examination of human or animal organs which are represented in virtual 3D space in order to perform a non-intrusive painless thorough examination. The desired navigation technique would further allow for a complete examination of a virtual organ in 3D space by an operator allowing flexibility while ensuring a smooth path and complete examination through and around the organ. It -would be additionally desirable to be able to display the exploration of the organ in a real time setting by using a technique which minitolzes the computations necessary for viewing the organ. The desired technique should also be equally applicable to exploring any virtual object SUMMARY The present invention relates to a system for performing virtual colonoscopy comprising: an imaging scanner for acquiring image data of a colon a processor, said processor receiving the image data and identifying the interior of the colon, generating a centerilnc for navigating through the interior of the colon, detecting a collapsed region of the interior of the colon, and extending the centeriine through the collapsed region; a display unit operatively coupled to the processor for displaying a representation of the colon.

The invention generates a three-dimensional visualization image of an object such as a human organ using volume visualization techniques and explores the virtual image using a guided navigation system which allows the operator to travel along a predefined flight path and to adjust both the position and viewing angle to a particular portion of interest in the image away from the predefined path in order to identify polyps, cysts or other abnormal features in the organ.

The inventive technique for three-dimensional virtual examination of an object includes producing a discrete representation of the object in volume elements, defining the portion of the object which is to be examined, performing a navigation operation in the virtual object and displaying the virtual object in real time during the navigation. 3a The inventive technique for ft three-dimensional virtual examination as applied to an organ of a patient includes preparing the organ for scanning, if necessary, scanning the organ and converting the data into volume elements, defining the portion of the organ which is to be exarnined, performing a guided navigation operation in the virtual organ and displaying the virtual organ in real time during the guided navigation. 4 In performing virtual examination, it is often desirable to view a particular material type while removing other material types from the image. To perform such an operation, a method for electronically cleansing an image can be performed by converting the image data to a plurality of volume elements with each volume element having an intensity value. Next , a classifying operation is performed to classify the volume elements into a plurality of clusters in accordance with the intensity values. Once classified, at least one cluster of volume elements can then be removed from the image data.

The classifying operation can be performed by evaluating a plurality of volume elements of the image data with respect to a plurality of neighboring volume elements to determine a neighborhood similarity value for the volume element The clusters can be further classified by applying a mixture probability ■ function to the clusters to classify voxels whose intensity value results from inclusion of more than one material type.

An alternative classifying operation includes the steps of perfbrming feature vector analysis on at least one of the clusters which include image data for a material of interest followed by performing high level feature extraction to remove volume elements from the image which are not substantially indicative of the material of interest.

The method according method for electronically cleansing an image is well suited for applications where the image data represents a region of the human body including at least a portion of the colon and the material of interest is tissue of a colon. In colon imaging applications, the removing operation can remove volume elements representing intracolonic fluid, residual stool within the colon, bone, and non-colonic tissue.

It is an object of the invention to use a system and method to perform a relatively painless, inexpensive and non-intrusive in vivo examination of an organ where the actual analysis of the scanned colon can be possibly performed without the patient present The colon can be scanned and visualized in real-time or the stored data can be visualized at a later time.

It is another object of the invention to generate 3D volume representations of an object, such as an organ, where regions of the object can be peeled back layer by layer in order to provide sub- surface analysis of a region of the imaged object A surface of an object (such as an organ) can be rendered transparent or translucent in order to view further objects within or behind the object wall. The object can also be sliced In order to examine a particular cross-section of (be object.

It is another object of the invention to provide a system and method of guided navigation through a 3D volume representation of an object, such as an organ, using potential fields.

It is a further object of the invention to calculate the center-line of an object, such as an organ, for a viriual fly-through using a peel-layer technique as described herein.

It is still a urther object of the invention to use a modified Z buffer technique to minimize the number of computations required for generating the viewing screen.

It is another object of the invention to assign opacity coefficients to each volume element in the representation in order to make particular volume elements transparent or translucent to varying degrees in order to customize the visualization of the portion of the object being viewed. A section of the object can also be composited using the opacity coefficients.

BRIEF DESCRIPTION OF THE DRAWINGS Further objects, features and advantages of the invention will become apparent from the following detailed description taken in conjunction with the accompanying figures showing a preferred embodiment of the invention, on which: Figure 1 is a flow chart of the steps for perforating a virtual examination of an object, specifically a colon, in accordance with the invention; Figure 2 is an illustration of a "submarine'' camera model which performs guided navigation in the virtual organ; Figure 3 is an illustration of a pendulum used to model pitch and roll of the "submarine" camera; 6 Figure 4 is a diagram illustrating a two dimensional cross-section of a volumetric colon which identifies two blocking walls; Figure 5 is a diagram illustrating a two dimensional cross-section of a volumetric colon upon which start and finish volume elements are selected Figure 6 is a diagram illustrating a two dimensional cross-section of a volumetric colon which shows a discrete sub-volume enclosed by the blocking walls and the colon surface; Figure 7 is a diagram illustrating a two dimensional cross-section of a volumetric colon which has multiple layers peeled away; Figure 8 is a diagram illustrating a two dimensional cross-section of a volumetric colon which contains the remaining flight path; Figure 9 is a flow chart of the steps of generating a volume visualization of the scanned organ; Figure 10 is an illustration of a virtual colon which has been subdivided into cells; Figure 11 A is a graphical depletion of as organ which is being virtually examined; Figure 1 IB is a graphical depiction of a stab tree generated when depicting the organ in Fig. 11 A; Figure 11C is a further graphical depiction of a stab tree generated while depicting the organ in Fig. 11 A; Figure 12A is a graphical depiction of a scene to be rendered with objects within certain cells of the scene; Figure 12B is a graphical depiction of a stab tree generated while depicting the scene in Fig. 12A; Figures 12C-12E are further graphical depictions of stab trees generated while depicting the image in Fig. 12A; Figure 13 is a two dimensional representation of a virtual colon containing a polyp whose layers can be removed; Figure 14 is a diagram of a system used to perform a virtual examination of a human organ in accordance with the invention; 7 Figure IS is a flow chart depicting an improved image segmentation method; Figure 16 is a graph of voxel intensity versus frequency of a typical abdominal CT data set; Figure 7 is a perspective view diagram of an intensity vector structure including a voxel of interest and its selected neighbors; Figure 18Λ is an exemplary image slice from a CT scan of a human abdominal region, primarily illustrating a region including the lungs; Figure 18B is a pictorial diagram illustrating the identification of the 10 lung region in the image slice of Figure ί 8 A; Figure 18C is a pictorial diagram illustrating the removal of the lung volume identified in Figure 18B; Figure 19A is a exemplary image slice form a CT scan of a human abdominal region, primarily illustrating a region including a portion of the colon and 15 bone; Figure 19B is a pictorial diagram illustrating the identification of the colon and bone region from the image slice of Figure 19 A; Figure 19C is a pictorial diagram illustrating the image scan of figure 119a with the regions of bone removed; and Figure 20 is a flowchart illustrating a method for applying texture to monochrome image data.

DET ILED DESCRIPTION While the methods and systems described in this application can be applied to any object to be examined, the preferred embodiment which will be 25 described is the examination of an organ in the human body, specifically the colon. The colon is long and twisted which makes it especially suited for a virtual examination saving the patient both money and the discomfort and danger of a physical probe. Other examples of organs which can be examined include the lungs, stomach and portions of the gastro-intestinal system, the heart and blood vessels. 8 Fig. 1 illustrates the steps necessary to perform a virtual colonoscopy using volume visualization techniques. Step 101 prepares the colon to be scanned in order to be viewed for examination if requited by either the doctor or the particular scanning instrument This preparation could include cleansing the colon with a "cocktail" or liquid which enters the colon after being orally ingested and passed through the stomach. The cocktail forces the patient to expel waste material that is present in the colon. One example of a substance used is Golytely. Additionally, in the case of the colon, air or CO, can be forced into the colon in order to expand it to make the colon easier to scan and examine. This is accomplished with a small tube placed in the rectum with approximately 1 ,000 cc of air pumped into the colon to distend the colon. Depending upon the type of scanner used, it may be necessary for the patient to drink a cont ast substance such as barium to coat any unexpunged stool in order to distinguish the waste in the colon from the colon walls themselves.

Alternatively, the method for virtually examining the colon can remove the virtual waste prior to or during the virtual examination as explained later in this specification. Step 101 does not need to be performed in all examinations as indicated by the dashed line in Fig. 1.

Step 103 scans the organ which is to be examined. The scanner can be an apparatus well known in the art, such as a spiral CT-scanner for scanning a colon or a Zenita M 1 machine for scanning a lung labeled for example with xenon gas. The scanner must be able to take multiple images from different positions around the body during suspended respiration, in order to produce the data necessary for the volume visualization. An example of a single CT-image would use an X-ray beam of 5mm width, 1:1 to 2:1 pitch, with a 40cm field-of-view being performed from the top of the splenic flexure of the colon to the rectum.

Discrete data representations of said object can be produced by other methods besides scanning. Voxel data representing an object can be derived from a geometric model by techniques described in U.S, Pat. No. 5,038,302 entitled "Method of Converting Continuous Three-Dimensional Geometrical Representations into Discrete Three-Dimensional Voxel-Based Representations Wrtbin a Three- Dimensional Voxel-Based System" by Kaufman, issued Aug. 8, 19 1, filed July 26, 1 88, which is hereby incorporated by reference. Additionally, data can be produced by a computer model of an image which can be converted to three-dimension voxels and explored in accordance with this invention. One example of this type of data is a computer simulation of the turbulence surrounding a space shuttle craft.

Step 104 converts the scanned images into three-dimensional volume elements (Voxels). In the preferred embodiment for examining a colon, the scan data is reformatted into 5mm thick slices at increments of I mm or 2 Jznm, with each slice represented as a matrix of 512 by 512 pixels. Thus a large number of 2D slices are generated depending upon the length of the scan. The set of 2D slices is then reconstructed to 3D voxels. The conversion process of 2D images from the scanner into 3D voxels can either be performed by the scanning machine itself or by a separate machine such as a computer with techniques which are well known in the art (for example, SS& U.S. Pat. No.4,985,856 entitled "Method and Apparatus for Storing, Accessing, and Processing Voxel-based Data" by Kaufman et al.; issued Jan. IS, 1991. filed Nov. 11, 1988; which is hereby incorporated by reference).

Step 105 allows the operator to define the portion of the selected organ to be examined. A physician may be interested in a particular section of the colon likely to develop polyps. The physician can view a two dimensional slice overview map to indicate the section to be examined. A starting point and finishing point of a path to be viewed can be indicated by the physician operator. A conventional computer and computer interface (e.g., keyboard, mouse or spaceball) can be used to designate the portion of the colon which is to be inspected. A grid system with coordinates can be used for keyboard entry or the physician operator can "click" on the desired points. The entire image of the colon can also be viewed if desired.

Step 107 performs the planned or guided navigation operation of the virtual organ being examined. Performing a guided navigation operation is defined as navigating through an environment along a predefined or automatically predetermined flight path which can be manually adjusted by an operator at any time. After the scan data has been convened to 3D voxels, the inside of the organ must be traversed from the selected start to the selected finishing point. The virtual examinations is modeled on having a tiny camera traveling through the virtual space with a lens pointing towards the finishing point The guided navigation technique provides a level of interaction with the camera, so that the camera can navigate through a virtual environment automatically in the case of no operator interaction, and at the same time, allow the operator to manipulate the camera when necessary. The preferred embodiment of achieving guided navigation is to use a physically based camera model which employs potential fields to control the movement of the camera and which are described in detail in Figs.2 and 3.

Step 109, which can be performed concurrentry with step 107, displays the inside of the organ from the viewpoint of the camera model along the selected pathway of the guided navigation operation. Three-dimensional displays can be generated using techniques well known in the art such as the marching cubes technique. However, in order to produce a real time display of the colon, a technique is required which reduces the vast number of computations of data necessary for the display of the virtual organ. Fig. 9 describe this display step in more detail.

The method described in Figure 1 can also be applied to scanning multiple organs in a body at the same time. For example, a patient may be examined for cancerous growths in both the colon and lungs. The method of Figure 1 would be modified to scan all the areas of interest in step 103 and to select the current organ to be examined in step 105. For example, the physician operator may initially select the colon to virtually explore and later explore the lung. Alternatively, two different doctors with different specialties may virtually explore different scanned organs relating to their respective specialties. Following step 109, the next organ to be examined is selected and its portion will be defined and explored. This continues until all organs which need examination have been processed.

The steps described in conjunction with Figure 1 can also be applied to the exploration of any object which can be represented by volume elements. For example, an architectural structure or inanimate object can be represented and explored in the same manner.

Figure 2 depicts a "submarine" camera control model which performs the guided navigation technique in step 107. When there is no operator control during guided navigation, the default navigation is similar to that of planned navigation which automatically directs the camera along a flight path from one selected end of the colon to another. During the planned navigation phase, the camera stays at the center of the colon for obtaining better views of the colonic surface. When an 11 interesting region is encountered, the operator of the virtual camera using guided navigation can interactively bring the camera close to a specific region and direct the motion and angle of the camera to study the interesting area in detail, without unwillingly colliding with the walls of the colon. The operator can control the camera with a standard interface device such as a keyboard, mouse or non-standard device such as a spaceball. In order to fully operate a camera in a virtual environment, six degrees of freedom for the camera is required. The camera must be able to move in the horizontal, vertical, and Z direction (axes 217), as well as being able to rotate in another three degrees of freedom (axes 21 ) to allow the camera to move and scan ad sides and angles of a virtual environment. The camera model for guided navigation includes an inextensible, weightless rod 201 connecting two particles x, 203 and J¼ 205, both particles being subjected to a potential field 215. The potential field is defined to be highest at the walls of the organ in order to push the camera away from the walls.

The positions of the panicles are given by x, and xz, and they are assumed to have the same mass m. A camera is attached at the head of the submarine x, 203, whose viewing direction coincides with x c,. The submarine can perform translation and rotation around the center of mass x of the model as the two particles are affected by the forces from the potential field V(x) which is defined below, any friction forces, and any simulated external force. The relations between x„ x2, and x are as follows: x = (XMZ), r - (ηϊηβαο5φΙηίηθίίηφ,ηο$Θ), x, - x + r, where r, Θ and φ are the polar coordinates of the vector xx,.

The kinetic energy of the model, T, is defined as the summation of the kinetic energies of the movements of x, and χ,: 12 τ = f« +*S) Then, the equations for the motion of the submarine model are obtained by using LaGrange's equation: -where the qjS are the generalised coordinates of the model and can be considered as the variables of time as: 9»i 9s, ?4, 96, ?e) = z, e* , ) = ¾(<)» (4) with ψ denoting the roW e«g/e of our camera system, which will be explained later. The F|S are called the generalized forces. The control of the submarine is performed by applying a simulated external force to x„ and it is assumed that both x, and x, are affected by the forces from the potential field and the frictions which act in the opposite direction of each particle's velocity. Consequently, the generalized forces are formulated as follows: P2. = mVV(xs) *- fci2j (5) where k denotes the friction coefficient of the system. The external force Fm is applied by the operator by simply clicking the mouse button in the desired direction 207 in the generated image, as shown in Figure 2. This camera model would then be 13 moved in that direction. This allows the operator to control at least five degrees of freedom of the camera with only a single click of the mouse button. From Equations (2), (3) and (5), it can be derived that the accelerations of the five parameters of our submarine model as: where x and x denote the first and the second derivative of x, respectively, and ( ¾T" ' ' ^Ί ~) den0tes ^ent οίϋχ Ρ°^^ * a P°«t x.

Theterms From the first three formulas of Equation (6), it is known that the submarine cannot be propelled by the external force against the potential field if the following condition is satisfied: Since the velocity of the submarine and the external force Fcal have upper limits in our implementation, by assigning sufficiently high potential values at the boundary of the objects, it can be guaranteed that the submarine never bumps against the objects or walls in the environment.

As mentioned previously, the roll angle ψ of the camera system needs to be considered. One possible option allows the operator full control of the angle ψ. However, although the operator can rotate the camera freely around the rod of the model, he or she can easily become disoriented. The preferred technique assumes that the upper direction of the camera is connected to a pendulum with mass m, 301, which rotates freely around the rod of the submarine, as shown in Figure 3. The direction of the pendulum, ¾ is expressed as: rj — ein i βη ψ). although it is possible to calculate the accurate movement of this pendulum along with the movement of the submarine, it makes the system equations too complicated. Therefore, it is assumed that all the generalized coordinates except the roll angle i|r are constants, and thus define the independent kinetic energy for the pendulum system as: This simplifies the model for the roll angle. Since it is assumed in this model that the gravitational force acts at the mass point /»-, the acceleration of ifr can be derived using LaGrange's equation as: φ ss — 0r(cos 0 cos cos^ — sin ^ sin.

+Sy (cos 6 sin coe φ 4- cos άη.·φ) + f(- sm ff cos^)> ». (7) From riquations (6) and (7), the generalized coordinates q(t) and their derivatives q(t) are calculated asymptotically by using Taylor series as: 16 q(t) + ¾q(t) + 0(¾'), to freely move the submarine. To smooth the submarine's motion, the time step h is selected as an equilibrium value between being as small as possible to smooth the motion but as large as necessary to reduce computation cose Pfffinflgn of the Potential Field The potential field in the submarine model in Figure 2 defines the boundaries (walls or other matter) in the virtual organ by assigning a high potential to the boundary in order to ensure that the submarine camera does not collide with the walls or other boundary. If the camera model is attempted to be moved into a high potential area by the operator, the camera model will be restrained from doing so unless the operator wishes to examine the organ behind the boundary or inside a polyp, for example. In the case of performing a virtual colonoscopy, a potential field value is assigned to each piece of volumetric colon data (volume element). When a particular region of interest is designated in step 105 of Fig. 1 with a start and finish point, the voxels within the selected area of the scanned colon are identified using conventional blocking operations. Subsequently, a potential value is assigned to every voxel x of the selected volume based an the following three distance values: the distance from the finishing point dt(x), the distance from the colon surface ds(x) and the distance from the center-line of the colon space dc(x). dt(x) is calculated by using a conventional growing strategy. The distance from the colon surface, ds(x), is computed using a conventional technique of growing from the surface voxels inwards. To determine dc(x), the center-line of the colon from the voxel is first extracted, and then dc(x) is computed using the conventional growing strategy from the center-line of the colon.

To calculate the center-line of the selected colon area defined by the usospecified start point and the user-specified finish point, the maximum value of ds(x) is located and denoted dmax. Then for each voxel inside the area of interest, a 17 cost value of dmax - ds(x) is assigned. Thus the voxels which are close to the colon surface have high cost values and the voxels close to the center line have relatively low cost values. Then, based on the cost assignment, the single-source shortest path technique which is well known in the art is applied to efficiently compute a minimum cost path from the source point to the finish point This low cost line indicates the center-line or skeleton of the colon section which is desired to be explored. This technique for determining the center-line is the preferred technique of the invention.

To compute the potential value V(x) for a voxel x inside the area of interest, the following formula is employed: where C„ C2, μ and v are constants chosen for the task. In order to avoid any collision between the virtual camera and the virtual colonic surface, a sufficiently large potential value is assigned for all points outside the colon. The gradient of the potential field will therefore become so significant that the submarine model camera will never collide with the colonic wail when being run.

Another technique to determine the center-line of the path in the colon is called the "peel-layer" technique and is shown in Figure 4 through Figure 8.

Figure 4 shows a 2D cross-section of the volumetric colon, with the two side walls 401 and 403 of the colon being shown. Two blocking walls are selected by the operator in order to define the section of the colon which is of mterest to examine. Nothing can be viewed beyond the blocking walls, This helps reduce the number of computations when displaying the virtual representation. The blocking walls together with side walls identify a contained volumetric shape of the colon which is to be explored.

Figure 5 shows two end points of the flight path of the virtual examination, the start volume element 501 and the finish volume element 503. The start and finish points are selected by the operator in step 1 OS of Fig. 1. The voxels between the start and finish points and the colon sides are identified and marked, as 18 indicated by the area designated with "x"s in Fig.6. The voxels are three-dimensional representations of the picture element.

The peel-layer technique is then applied to the identified and marked voxels in Fig. 6. The outermost layer of all the voxels (closest to the colon walls) is peeled off step-by-step, until there is only one inner layer of voxels remaining. Stated differently, each voxel furthest away from a center point is removed if the removal does not lead to a disconnection of the path between the start voxel and the finish voxel. Figure 7 shows the intermediate result after a number of iterations of peeling the voxels in the virtual colon are complete. The voxels closest to the walls of the colon have been removed. Fig. 8 shows the final flight path for the camera model down the center of the colon after all the peeling iterations are complete. This produces essentially a skeleton at the center of the colon and becomes the desired flight path for the camera model.

Z- Buffer assisted visihilitv Figure 9 describes a real time visibility technique to display of virtual images seen by the camera model in the virtual three-dimensional volume representation of an organ. Figure 9 shows a display technique using a modified Z buffer which corresponds to step 109 in Fig. 1. The number of voxels which could be possibly viewed from the camera model is extremely large. Unless the total number of elements (or polygons) which must be computed and visualized is reduced from an entire set of voxels in the scanned environment, the overall number of computations will make the visualization display process exceedingly slow for a large internal area. However, in the present invention only those images which are visible on the colon surface need to be computed for display. The scanned environment can be subdivided into smaller sections, or cells. The Z buffer technique then renders only a portion of the cells which are visible from the camera. The Z buffer technique is also used for three-dimensional voxel representations. The use of a modified Z buffer reduces the number of visible voxels to be computed and allows for the real time examination of the virtual colon by a physician or medical technician.

The area of interest from which the center-line has been calculated in step 107 is subdivided into cells before the display technique is applied. Cells are collective groups of voxels which become a visibiliry unit The voxels in each cell 19 will be displayed as a group, Each cell contains a number of portals through which the other cells can be viewed. The colon is subdivided by beginning at the selected start point and moving along the center-line 001 towards the finish point. The colon is then partitioned into cells (for example, cells 1003, 1005 and 007 in Fig. 10) when a predefined threshold distance along the center-path is reached. The threshold distance is based upon the specifications of the platform upon which the visualization technique is performed and its capabilities of storage and processing- The cell size is directly related to the number of voxels which can be stored and processed by the platform. One example of a threshold distance is 5cm, although the distance can greatly vary. Each cell has two cross-sections as portals for viewing outside of the cell as shown in Fig. 10.

Step 901 is Fig. 9 identifies the cell within the selected organ which currently contains the camera. The current cell will be displayed as well as all other cells which are visible given the orientation of the camera. Step 903 builds a stab tree (tree diagram) of hierarchical data of potentially visible cells from the camera (through defined portals), as will be described in further detail hereinbelow. The stab tree contains a node for every cell which may be visible to the camera. Some of the cells may be transparent without any blocking bodies present so that more than one cell will be visible in a single direction. Step 90S stores a subset of the voxels from a cell which include the intersection of adjoining cell edges and stores them at the outside edge of the stab tree in order to more efficiently determine which cells are visible.

Step 907 checks if any loop nodes are present in the stab tree. A loop node occurs when two or more edges of a single cell both border on the same nearby cell. This may occur when a single cell is surrounded by another cell. If a loop node is identified in the stab tree, the method continues with step 909. If there is no loop node, the process goes to step 911.

Step 909 collapses the two cells making up the loop node into one large node. The stab tree is then corrected accordingly. This eliminates the problem of viewing the same cell twice because of a loop node. The step is performed on all identified loop nodes. The process then continues with step 911.

Step 911 then initiates the Z-buffer with the largest Z value. The Z value defines the distance away from the camera along the skeleton path. The tree is then traversed to first check the intersection values at each node. If a node intersection is covered, meaning that the current portal sequence is occluded (which is determined by the Z buffer test), then the traversal of the current branch in the tree is stopped Step 913 traverses each of the branches to check if the nodes are covered and displays them if they are not Step 915 then constructs the image to be displayed on the operator's screen from the volume elements within the visible cells identified in step 913 using one of a variety of techniques known in the art, such as volume rendering by compositing. The only cells shown are those which are identified as potentially visible. This technique limits the number of cells which requires calculations in order . to achieve a real time display and correspondingiy increases the speed of the display for better performance. This technique is an improvement over prior techniques which calculate all the possible visible data points whether or not they are actually viewed.

Figure 11 A is a two dimensional pictorial representation of an organ which is being explored by guided navigation and needs to be displayed to an operator. Organ 1101 shows two side walls 1102 and an object 1105 in the center of the pathway. The organ has been divided into four cells A 1151, B 1153, C 1155 and D 1157. The camera 1103 is racing towards cell D 1157 and has a field of vision defined by vision vectors 1107, 1108 which can identify a cone-shaped field. The cells which can be potentiiUly viewed are cells B 1153, C 1155 and D 1157. Cell C 1155 is completely surrounded by Cell B and thus constitutes a node loop.

Fig. 1 IB is a representation of a stab tree built from the cells in Fig. 11 A. Node A 1109 which contains the camera is at the toot of the tree. A sight line or sight cone, which is a visible path without being blocked, is drawn to node B 1110. Node B has direct visible sight lines to both node C 1112 and node D 1114 and which is shown by the connecting arrows. The sight line of node C 1112 in the direction of the viewing camera combines with node B 1110. Node C 1112 and node B 1110 will thus be collapsed into one large node B' 1122 as shown in Fig. 11C. 21 Fig. 11 C shows node A 1109 containing the camera adjacent to node B' 1122 (containing both nodes B and node C) and node D 1114. The nodes A, B' and D will be displayed at least partially to the operator.

Figs 12A - 12E illustrate the use of the modified Z buffer with cells that contain objects which obstruct the views. An object could be some waste material in a portion of the virtual colon. Fig. 12A shows a virtual space with 10 potential cells: A 1251, B 1253, C 1255, D 1257, E 1259, F 1261, G 1263, H 1265, I 1267 and J 1269. Some of the cells contain objects. If the camera 1201 is positioned in cell 1 1267 and is facing toward cell F 1261 as indicated by the vision vectors 1203, then a stab tree is generated in accordance with the technique illustrated by the flow diagram in Fig. 9. Fig. 12B shows the stab tree generated with the intersection nodes showing for the virtual representation as shown in Fig. 12 A. Fig. 12B shows cell 1 1267 as the root node of the tree because h contains- the camera 1201. Node l 1211 is pointing to node F 1213 (as indicat d with an airow), because cell F is directly connected to the sight line of the camera. Node F 1213 is pointing to both node B 1215 and node £ 1219. Node B 1215 is pointing to uode.A 1217. Node C 1202 is completely blocked from the line of sight by camera 1201 so is not included in the stab tree.

Fig. 12C shows the stab tree after node I 1211 is rendered on the display for the operator. Node 1 1211 is then removed from the stab tree because it has already been displayed and node F 1213 becomes the roo Fig. 12D shows that node F 1213 is now rendered to join node 1 1211. The next nodes in the tree connected by arrows are then checked to see if they are already covered (already processed). In this example, all of the intersected nodes from the camera positioned in cell 1 1267 has been covered so that node B 515 (and therefore dependent node A) do not need to be rendered on the display.

Fig. 12E shows node E 515 being checked to determine if its intersection has been covered. Since it has, the only rendered nodes in this example of Figure 12A-12E are nodes I and F while nodes A, B and E arc not visible and do not need to have their cells prepared to be displayed. 22 The modified Z buffer technique described in Figure 9 allows for fewer computations and can be applied to an object which has been represented by voxels or other data elements, such as polygons.

Figure 13 shows a two dimensional virtual view of a colon with a large polyp present along one of its walls. Figure 13 shows a selected section of a patient's colon which is to be examined further. The view shows two colon walls 1301 and 1303 with the growth indicated as 1 05. Layers 1307, 1309, and 1311 show inner layers of the growth. It is desirable for a physician to be able to peel the layers of the polyp or tumor away to look inside of the mass for any cancerous or other harmful material. This process would in effect perform a virtual biopsy of the mass without actually cutting into the mass. Once the colon is represented virtually by voxels, the process of peeling away layers of an object is easily performed in a similar manner as described in conjunction with Figs.4 through 8. The mass can also be sliced so that a particular cross-section can be examined. In Fig. 13 , a planar cut 1313 can be made so that a particular portion of the growth can be examined. Additionally, a user* defined slice 1319 can be made in any manner in the growth. The voxels 1319 can either be peeled away or modified as explained below.

A transfer function can be performed to each voxel in the area of interest which can make the object transparent, semi-transparent or opaque by altering coefficients representing the translucently for each voxel. An opacity coefficient is assigned to each voxel based on its density. A mapping function then transforms the density value to a coefficient representing its translucency. A high density scanned voxel will indicate either a wall or other dense matter besides simply open space. An operator or program routine could then change the opacity coefficient of a voxel or group of voxels to make them appear transparent or serai-transparent to the submarine camera model. For example, an operator may view a tumor within or outside of an entire growth. Or a transparent voxel will be made to appear as if it is not present for the display step of Figure 9. A composite of a section of the object can be created using a weighted average of the opacity coefficients of the voxels in that section.

If a physician desires to view the various layers of a polyp to look for a cancerous areas, this can be performed by removing the outer layer of polyp 1305 yielding a first layer 1307. Additionally, the first inner layer 1307 can be stripped 23 back to view second inner layer 1309. The second inner layer can be stripped back to view third inner layer 1311, etc. The physician could also slice the polyp 130S and view only those voxels within a desired section. The slicing area can be completely user-defined.

Adding an opacity coefficient can also be used in other ways to aid in the exploration of a virtual system. If waste material is present and has a density as other properties within a certain known range, the waste can be made transparent to the virtual camera by changing its opacity coefficient during the examination. This will allow the patient to avoid ingesting a bowel cleansing agent before the procedure and make the examination faster and easier. Other objects can be similarly made to disappear depending upon the actual application. Additionally, some objects like polyps could be enhanced electronically by a contrast agent followed by a use of an appropriate transfer function.

Figure 14 shows a system for performing the virtual examination of an object such as a human organ using the techniques described in this specification. Patient 1401 lies down on a platform 1402 while scanning device 140S scans the area that contains the organ or organs which are to be examined. The scanning device 1405 contains a scanning portion 1403 which actually takes images of the patient and an electronics portion 1406. Electronics portion 1406 comprises an interface 1407, a central processing unit 1409, a memory 1411 for temporarily storing me scanning data, and a second interface 1413 for sending data to the virtual navigation platform. Interface 1407 and 1413 could be included in a single interface component or could be the same component. The components in portion 1406 are connected together with conventional connectors.

In system 1400, the data provided from the 1403 is transferred to portion 1405 for processing and is stored in memory 1411. Central processing unit 1 09 converts the scanned 2D data to 3D voxel data and stores the results in another portion of memory 1411. Alternatively, the converted data could be directly sent to interface unit 1413 to be transferred to the virtual navigation terminal 1416. The conversion of the 2D data could also take place at the virtual navigation terminal 1416 after being transmitted from interface 1413. In the preferred embodiment, the converted d-ta is transmitted over carrier 1 14 to the virtual 24 navigation terminal 1416 in order for an operator to perform the virtual exarninarJon. The data could also be transported is other conventional ways such as storing the data on a storage medium and physically transporting it to terminal 1416 or by using satellite transmissions.

The scanned data may not be converted to its 3D representation until the visualization rendering engine requires it to be in 3D form. This saves computational steps and memory storage space.

Virtual navigation terminal 1416 includes a screen for viewing the virtual organ or other scanned image, an electronics portion 1415 and interface control 1419 such as a keyboard, mouse or spaceball, Electronics portion 1415 comprises a interface port 1421, a central processing unit 1423, other components 1427 necessary to run the terminal and a memory 1425. The components in terminal 1 16 are connected together with conventional connectors. The converted voxel data is received in interface port 421 and stored in memory 1425. The central processor unit 1423 then assembles the 3D voxels into a virtual representation and runs the submarine camera model as described in Figures 2 and 3 to perform the virtual examination. As the submarine camera travels through the virtual organ, the visibility technique as described in Figure 9 is used to compute only those areas which are visible from the virtual camera and displays them on screen 1417. A graphics accelerator can also be used in generating the representations. The operator can use interface device 141 to indicate which portion of the scanned body is desired to be explored. The interface device 1419 can further be used to control and move the submarine camera as desired as discussed in Figure 2 and its accompanying description. Terminal portion 1415 can be the Cube-4 dedicated system box, generally available from the Department of Computer Science at the State University of New York at Stony Brook.

Scanning device 1405 and terminal 1416, or parts thereof, can be part of the same unit A single platform would be used to receive the scan image data, connect it to 3D voxels if necessary and perform the guided navigation.

An important feature in system 1400 is that the virtual organ can be examined at a later time without the presence of the patient Additionally, the virtual examination could take place while the patient is being scanned. The scan data can also be sent to multiple terminals which would allow more than one doctor to view the inside of the organ simultaneously. Thus a doctor in Mew York could be looking at the same portion of a patient's organ at the same time with a doctor in California while discussing the case. Alternatively, the data can be viewed at different times. Two or more doctors could perform their own examination of the same data in a difficult case. Multiple virtual navigation terminals could be used to view the same seen data. By reproducing the organ as a virtual organ with a discrete set of data, there are a multitude of benefits in areas such as accuracy, cost and possible data manipulations.

The above described techniques can be further enhanced in virtual colonoscopy applications through the use of an improved electronic colon cleansing technique which employs modified bowel preparation operations followed by image segmentation operations, such that fluid and stool remaining in the colon during a computed tomographic (CT) or magnetic resonance imaging (MRI) scan can be detected and removed from the virtual colonoscopy images. Through the use of such techniques, conventional physical washing of the colon, and its associated inconvenience and discomfort, is roinimjzed or completely avoided.

Referring to Figure 15, the first step in electronic colon cleansing is bowel preparation (step 1510), which takes place prior to conducting the CT or magnetic resonance imaging ( RI) scan and is intended to create a condition where residual stool and fluid remaining in the colon present significantly different image properties from that of the gas-filled colon interior and colon wall. An exemplary bowd preparation operation includes ingesting three 250 cc doses of Barium Sulfate suspension of 2.1 % W V, such as manufactured by E-2-EM, Inc,of Westbury, New York, during the day prior the CT or MRI scan. The three doses should be spread out over the course of the day and can be ingested along with three meals, respectively. The Barium Sulfate serves to enhance the images of any stool which remains in the colon. In addition to the intake of Barium Sulfate, fluid intake is preferably increased during the day prior to the CT or MRI scan. Cranberry juice is known to provide increased bowel fluids and is preferred, although water can also be ingested. In bom the evening prior to the CT scan and the morning of the CT scan, 60 ml of a Diatrizoate Meglumine and Diaztrizoate Sodium Solution, which is commercially available as MDOastroview, manufactured by Mallinckrodt, Inc. of St. Louis, 26 Missouri, can be consumed to enhance image properties of the colonic fluid. Sodium phosphate can also be added to the solution to liquilize the stool in the colon, which provides for more uniform enhancement of the colonic fluid and residual stooL The above described exemplary preliminary bowel preparation operation can obviate the need for conventional colonic washing protocols, which can call for the ingestion of a gallon of Golytely solution prior to a CT scan.

Just prior to conducting the CT scan, an intravenous injection of 1 ml of Glucagon, manufactured by Ely Lily and Company, of Indianapolis, Indiana can be administered to minimize colon collapse. Then, the colon can be inflated using approximately lOOOcc of compressed gas, such as COj, or room air, which can be introduced through a rectum tube. At this point, a conventional CT scan is performed to acquire data from the region of the colon (step 1 S20). For example, data can be acquired using a GE CTI spiral mode scanner operating in a helical mode of 5mm, 1.5-2,0:1 pitch, where the pitch is adjusted based upon the patient's height in a known manner. A routine imaging protocol of 120 kVp and 200-280 a can be utilized for this operation. The data can be acquired and reconstructed as 1mm thick slice images having an array size of 512x512 pixels in the field of view, which varies from 34 to 40 cm depending on the patient's size, the number of such slices generally varies under these conditions from 300 to 450, depending on the patient's height The image data set is converted to volume elements or voxels (step 1530).

Image segmentation can be performed in a number of ways. In one present method of image segmentation, a local neighbor technique is used to classi y voxels of the image data in accordance with similar intensity values. In this method, each voxel of an acquired image is evaluated with respect to a group of neighbor voxels. The voxel of interest is referred to as the central voxel and has an associated intensity value, A classification indicator for each voxel is established by comparing the value of the central voxel to each of its neighbors. If the neighbor has the same value as the central voxel, the value of the classification indicator is incremented. However, if the neighbor has a different value from the central voxel, the classification indicator for the central voxel is decremented. The central voxel is then classified to that category which has the maximum indicator value, which indicates the most Uniform neighborhood among the local neighbors. Each classification is 27 indicative of a particular intensity range, which in turn is representative of one or more materia) types being imaged. The method can be further enhanced by employing a mixture probability function to the similarity classifications derived An alternate process of image segmentation is performed as two major operations: low level processing and high level feature extraction. During low level processing, regions outside the body contour are eliminated from further processing and voxels within the body contour are roughly categorized in accordance with well defined classes of intensity characteristics. For example, a CT scan of the abdominal regioa generates a data set which tends to exhibit a well defined intensity distribution. The graph of Figure 16 illustrates such an intensity distribution as an exemplary histogram having four, well defined peaks, 1602, 1604, 1606, 1608, which can be classified according to intensity thresholds.

The voxels of the abdominal CT data set are roughly classified as four clusters by intensity thresholds (step 1540). For example, Cluster 1 can include voxels whose intensities are below 140. This cluster generally corresponds to the lowest density regions within the interior of the gas filled colon. Cluster 2 can include voxels which have intensity values in excess of 2200. These intensity values correspond to the enhanced stool and fluid within the colon as well as bone. Cluster 3 can include voxels with intensities in the range of about 900 to about 1080. This intensity range generally represents soft tissues, such as fat and muscle, which are unlikely to be associated with the colon. The reniaining voxels can then be grouped together as cluster 4, which are likely to be associated with the colon wall (including mucosa and partial volume mixtures around the colon wait) as well as lung tissue and soft bones.

Clusters 1 and 3 are not particularly valuable in iderrtifying the colon wall and, therefore are not subject to substantia] processing during image segmentation procedures for virtual colonoscopy. The voxels associated with cluster 2 are important for segregating stool and fluid from the colon wall and are processed further during the high-level feature extraction operations. Low level processing is concentrated on the fourth cluster, which has the highest likelihood of corresponding to colon tissue (step 1550). 28 For each voxel in the fourth cluster, an intensity vector is generated using itself and its neighbors. The intensity vector provides an indication of the change in intensity in the neighborhood proximate a given voxel. The number of neighbor voxels which are used to establish the intensity vector is not critical, but involves a tradeoff between processing oveihead and accuracy. For example, a simple voxel intensity vector can be established with seven (7) voxels, which includes the voxel of interest, its front and back neighbors, hs left and right neighbors and its top and bottom neighbors, all surrounding the voxel of interest on three mutually perpendicular axes. Figure 17 is a perspective view illustrating an exemplary intensity vector in the form of a 25 voxel intensity vector model, which includes the selected voxel 1702 as well as its first, second and third order neighbors. The selected voxel 1702 is the central point of this model and is referred to as the fixed voxel, A planar slice of voxels, which includes 12 neighbors on the same plane as the fixed voxel, is referred to as the fixed slice 1704. On adjacent planes to the fixed slice are two nearest slices 1706, having five voxels each. Adjacent to the first nearest slices 1706 are two second nearest slices 1708, each having a single voxel. The collection of intensity vectors for each voxel in the fourth cluster is referred to as a local vector series.

Because the data set for an abdominal image generally includes more than 300 slice images, each with a 512 x 512 voxel array, and each voxel having an associated 25 voxel local vector, it is desirable to perform feature analysis (step 1570) on the local vector series to reduce the computational burden. One such feature analysis is a principal component analysis (PCA), which can be applied to the local vector series to determine the dimension of a feature vector series and an orthogonal transformation matrix for the voxels of cluster 4.

It has been found that the histogram (Figure 16) of the CT image intensities tends to be fairly constant from patient to patient for a particular scanner, given equivalent preparation and scanning parameters. Relying on this observation, an orthogonal trarisformation matrix can be established which is a rjredetermined matrix determined by using several sets of training data acquired using the same scanner under similar conditions. From this data, a transformation matrix, such as a arOiunen-Loeve ( -L) traruformation, can be generated in a known manner. The 29 transformation matrix is applied to the local vector series to generate feature vector series- Once in the feature-vector space domain, vector quantization tecbtuques can be used to classify the feature vector series.

An analytical, self-adaptive algorithm can be used for the classification of the feature vectors. In defining this algorithm, let {Xj€ 4:i » 1,2,3,...,N} be the series of the feature vectors, where N is the number of feature vectors; denotes the maximum number of classes; and T is a threshold which is adaptive to the data set. For each class, a representative element is generated by the algorithm. Let at be a representative element of class k and ¾ be the number of feature vectors in that class.

The algorithm can then be outlined as: 1. Set ηχ=1; a^X^K*!-. 2. obtain the class number and class parameters (a, j for "=l;/«I;i++) for U=l,- j for (j =li j<¾ j + * | calculate dj -dist (xif a^) ; end for index = arc min dtt label voxel to class index. end for In this algorithm, disifcy) is the Euclidean distance between vector x and y and arc rain d, gives the integer / which realizes the minimum value of &r The above described algorithm is dependent only on the parameters Tand K. However, the value of , which relates to the number of classes within each voxel cluster, is not critical and can be set to a constant value, such as =l8. However, T, which is the vector similarity threshold, greatly influences the classification results. If the selected value of T is too large, only a single class will be generated. On the other hand, if the value of T is too small, the resulting classes will exhibit undesirable redundancy. By setting the value of T to be equal to die maximum component variance of the feature vector series, the maximum number of distinct classes results.

As a result of the initial classification process, each voxel within the selected cluster is assigned to a class (step 1570). In the exemplary case of virtual colonoscopy, there are several classes within cluster 4. Thus, the next task is to determine which of the several classes in cluster 4 corresponds to the colon wall. The first coordinate of the feature vector, which is that coordinate of the feature vector exhibiting the highest variance, reflects the information of the average of the 3D local voxel intensities. The remaining coordinates of the feature vector contain the information of directional intensity change within the local neighbors. Because the colon wall voxels for the interior of the colon are generally in close proximity to the 31 gas voxels of cluster 1, a threshold interval can be determined b data samples selected from typical colon wall intensities of a typical CT data set to roughly distinguish colon wall voxel candidates. The particular threshold value is selected for each particular imaging protocol and device. This threshold interval can then applied to all CT data sets (acquired from the same machine, using the same imaging protocol). If the first coordinate of the representative element is located in the threshold interval, the corresponding class is regarded as the colon wall class and all voxels in that class are labeled as colon wall-like voxels.

Each colon wall-like voxel is a candidate to be a colon wall voxel. There are three possible outcomes of not belonging to the colon wall. The first case relates to voxels which are close to the stool/liquid inside the colon. The second case occurs when voxels are in the lung tissue regions. The third case represents mucosa voxels, Clearly then, low level classification carries a degree of classification uncertainty. The causes of the low-level classification uncertainty vary. For example, a partial- volume effect resulting from voxels contau ing more than one material type (i.e., fluid and colon wall) leads to the first case of uncertainty. The second and the third cases of uncertainty are due to both the partial volume effect as well as the low contrast of CT images. To resolve the uncertainty, additional information is needed. Thus, a high-level feature extraction procedure is used in the present method to further distinguish candidates for the colon wall from other colon wall-like voxels, based on a priori anatomical knowledge of the CT images (step 1580).

An initial step of the high-level feature extraction procedure can be to eliminate the region of lung tissue from the low-level classification results. Figure 18Λ is an exemplary slice image clearly LUustrating the lung region 1802. The lung region 1802 is identifiable as a generally contiguous three dimensional volume enclosed by colon wall-like voxels, as illustrated in Figure 18B. Given mis characteristic, the lung region can be identified using a region growing strategy. The first step in this technique is to find a seed voxel within the region of growing.

Preferably, the operator performing the CT imaging scan sets the imaging range such that the top most slice of the CT scan does not contain any colon voxels. As the interior of lung should be filled with air, the seed is provided by the low-level classification simply by selecting an air voxel. Once the lung region outline of Figure 32 1 BB is determined, the lung volume can be removed from the image slice (Figure 18C).

A next step in performing high-level feature extraction can be to separate the bone voxels from enhanced stool/fluid voxels in chister 2. The bone tissue voxels 1902 are generally relatively far away from the colon wall and resides outside the colon volume. To the contrary, the residual stool 1 06 and fluid 1904 are enclosed inside the colon volume, Combining the a priori proximity information and the colon wall information obtained from the low-level classification process, a tough colon wall volume is generated. Any voxel separated by more than a predetermined number (e.g., 3) of voxel units from the colon wall, and outside the colon volume, will be labeled as bone and then removed rom the image. The remaining voxels in cluster 2 can be assumed to represent stool and fluid within the colon volume (see Figures 1 A-C).

The voxels within the colon volume identified as stool 1 06 and fluid 1904 can be removed from the image to generate a clean colon lumen and colon wall image. In general, there are two kinds of stool fluid regions. One region type is small residual areas of stool 1906 attached to the colon wall. The other region type is large volumes of fluid 1 04, which collect in basin-like colonic folds (see Figures 19A-C).

The attached residual stool regions 1906 can be identified and removed because they are inside the rough colon volume generated during the low-level classification process. The fluid 1 06 in the basin-like colon fold usually has a horizontal surface 1908 due to the effect of gravity. Above the surface is always a gas region, which exhibits a very high contrast to the fluid intensity. Thus, the surface interface of the fluid regions can be easily marked.

Using a region growing strategy, the contour of the attached stool regions 1906 can be outlined, and the part which is away from the colon wall volume can be removed. Similarly, the contour of the fluid regions 1904 can also be outlined. After eliromating the horizontal surfaces 1908, the colon wall contour is revealed and the clean colon wall is obtained.

It is difficult to distinguish the mucosa voxels from the colon wall voxels. Even though the above three dimensional processing can remove some mucosa voxels, it is difficult to remove all mucosa voxels. In optical colonoscopy, 33 physicians directly inspect the colonic mucosa and search for lesions based on the color and texture of the mucosa. In virtual colonoscopy, most mucosa voxels on the colon wall can be left intact in order to preserve more information. This can be very useful for three dimensional volume rendering.

From the segmented colon wall volume, the inner surface, the outer surface and the wall itself of the colon can be extracted and viewed as a virtual object. This provides a distinct advantage over conventional optical colonoscopy in that the exterior wall of the colon can be examined as well as the interior wall. Furthermore, the colon wall and the colon lumen can be obtained separately from the segmentation.

Because the colon is substantially evacuated prior to imaging, a commonly encountered problem is thai the colon lumen collapses in spots. While the inflation of the colon with compressed gas, such as air or C02, reduces the frequency of collapsed regions, such areas still occur. In performing a virtual colonoscopy, it is desirable to automatically maintain a flight path through the collapsed regions and it is also desirable to use the scanned image data to at least partially recreate the colon lumen in the collapsed regions. Since the above described image segmentation methods effectively derive both the interior and exterior of the colon wall, this information can be used to enhance the generation of the fly path through the collapsed regions.

In extending the flight path through collapsed regions of the colon or expanding a collapsed region of the colon, the first step is to detect a collapsed region. Using the premise that the grayscale values of the image data from around the outside of the colon wall change much more dramatically than the grey scale values within the colon wall itself, as well as in other regions such as fat, muscle and other kinds of tissue, an entropy analysis can be used to detect areas of colon collapse.

The degree of change in grayscale value, for example along the centerline, can be expressed and measured by an entropy value. To calculate an entropy value, voxels on the outer surface of the colon wall are selected. Such points are identified from the above described image segmentation techniques. A 5x5x5 cubic window can be applied to the pixels, centered on the pixel of interest Prior to calculating the entropy value, a smaller (3x3x3) window can be applied to the pixels 34 of interest in order to filter out noise from the image data. The entropy value of a selected window about the pixel can then be detennined by the equation: E=∑ C( i) ln ( C( il ) 1 where £ is the entropy and C(i) is the number of points in the window with the grayscale of i 0=0,1,2,. . 255). The calculated entropy values for each window are then compared against a predetermined threshold value. For regions of air, the entropy values will be fairly low, when compared to regions of tissue. Therefore, along the centerline of the colon lumen, when the entropy values increase and exceed the predetermined threshold value, a collapsed region is indicated. The exact value of the threshold is not critical and will depend in part on the imaging protocol and particulars of the imaging device.

Once a collapsed region is detected, the previously determined centerline flight path can be extended through the region by piercing through the center of the collapse with a one voxel wide navigation line.

In addition to automatically continuing the flight path of the virtual camera through the colon lumen, the region of colon collapse can be virtually opened using a physical modeling technique to recover some of the properties of the collapsed region. In this technique, a model of the physical properties of the colon wall is developed. From this model, parameters of motion, mass density, damping density, stretching and bending coefficients are estimated for a Lagrange equation. Then, an expanding force model (i.e., gas or fluid, such as air, pumped into the colon) is formulated and applied in accordance with the elastic properties of the colon, as defined by the Lagrange equation, such that the collapsed region of the colon image is restored to its natural shape.

To model the colon, a finite-element model can be applied to the collapsed or obstructed regions of the colon lumen. This can be performed by sampling the elements in a regular grid, such as an 8 voxel brick, and then applying traditional volume rendering techniques. Alternatively, an irregular volume representation approach, such as tetrahedrons can be applied to the collapsed regions. Ια applying the external force (air pumping) model to the colon model, the magnitude of the external force is first determined to properly separate the collapsed colon wall regions. A three dimensional growing model can be used to trace the internal and external colon wall surfaces in a parallel manner. The respective surfaces are marked from a starting point at the collapsed region to a growing source point, and the force model is applied to expand the surfaces in a like and natural manner. The region between the internal and external surfaces, i.e., the colon wall, are classified as sharing regions. The external repulsive force model is applied to these sharing regions to separate and expand the collapsed colon wall segments in a natural manner.

To more clearly visualize the features of a virtual object, such as the colon, which is subjected to virtual examination, it is advantageous to provide a rendering of the various textures of the object, Such textures, which can be observed in the color images presented during optical colonoscopy, are often lost in the black and white, grey scale images provided by the CT Image data. Thus a system and method for texture imaging during virtual examination is required.

Figure 20 is a flow chart depicting a present method for generating virtual objects having a texture component. The purpose of this method is to map textures obtained by optical colonoscopy images in the red-green-blue (RGB) color space, as for example from the Visible Human, onto the gray scale monochrome CT image data used to generate virtual objects/ The optical colonoscopsy images are acquired by conventional digital image acquistion techniques, such as by a digital "frame grabber" 1429 which receives analog optical images from a camera, such as a video camera, and converts the image to digital data which can be provided to CPU 1423 via interface port 1431 (Figure 14). The first step in this process is to segment the CT image data (step 2010). The above described image segmentation techniques can be applied to choose intensity thresholds in the grey scale image to classify the CT image data into various tissue types, such as bone, colon wall tissue, air, and the like.

In addition to petfonning image segmentation on the CT image data, the texture features of the optical image need to be extracted from the optical image data (step 2020). To do this, a gausian filter can be applied to the optical image data followed by subsamphng to decompose the data into a multiresolutional pyramid. A 36 laplacian filter and steerable filter can also be applied to the multiresolutional pyramid to obtain oriented and non-oriented features of the data. While this method is effective at extracting and capturing the texture features, the implementation of mis approach requires a large amount of memory and processing power.

An alternative approach to extracting the texture features from the optical image is to utilize a wavelet transform. However, while wavelet transformations are generally computationally efficient, conventional wavelet transforms are limited in that they only capture features with orientations parallel to the axes and cannot be applied directly to a region of interes To overcome these limitations, a non-separable filter can be employed. For example, a lifting shcrne cam be employed to build filter banks for wavelets transform in any dimension using a two step, prediction and updating app oach. Such filter banks can be synthesized by the Boor-Rom algorithm for multidimensional polynomial interpolation.

After the textural features are extracted from the optical image data- models must be generated to describe these features (step 2030). This can be performed, for example, by using a non-parametric multi-scale statistical model which is based on estimating and manipulating the entropy of non-Gaussian distributions attributable to the natural textures.

Once texture models are generated from the optical image data, texture matching must be performed to correlate these models to the segmented CT image data (step 2050). In regions of the CT image data where the texture is continuous, corresponding classes of texture are easily matched. However, in boundary regions between two or more texture regions, the process is more complex. Segmentation of the CT data around a boundary region often leads to data which is fuzzy, i.e., the results reflect a percentage of texture from each material or tissue and vary depending on me various weighting of each. The weighting percentage can be used to set the importance of matching criteria.

In the case of the non-parametric multi-scale statistical model, the cross entropy or a ullback-Leiber divergence algorithm can be used to measure the distribution of different textures in a boundary region. 37 After texture matching, texture synthesis is performed on the CT image data (step 2050). This is done y fusing the textures from the optical image data in to the CT image data. For isotropic texture patterns, such as presented by bone, the ¾xture can be sampled directly from the optical data to the segmented CT image data. For unisotropic texture regions, such as colon mucosa, a multiresolution sampling procedure is preferred. In this process, selective resampling for homogenous and heterogenous regions is employed.

In addition to enhanced imaging, the above described techniques can also form the basis of a system for performing virtual electronic biopsy of a region being examined to effect a flexible and non-invasive biopsy. Volume rendering techniques employ a defined transfer function to map different ranges of sample values of the original volume data to different colors and opacities. Once a suspicious area is detected during virtual examination, the physician can interactively change the transfer function used during the volume rendering procedure such that the wall being viewed becomes substantially transparent and the interior of the area can be viewed.

In addition to performing virtual biopsy, the present system and methods can be extended to perform automated polyp detection. Polyps which occur, for example, within the colon, are generally small convex hill-like structures extending from the colon wall. This geometry is distinct from the fold of the colon wall. Thus, a differential geometry model can be used to detect such polyps on the colon wall.

The surface of the colon lumen can be represented using a C-2 smoothness surface model In this model, each voxel on the surface has an associated geometrical feature which has a Gaussian curvature, referred to as Gaussian curvature fields. A convex hill on the surface, which may be indicative of a polyp, possesses a unique local feature in the Gaussian curvature fields. Accordingly, by searching the Causian curvature fields for specific local features, polyps can be detected.

Each of the foregoing methods can be implemented using a system as illustrated in Figure 14, with appropriate software being provided to control the operation of CPU 1409 and CPU 1423.

The foregoing merely illustrates Ihe principles of the invention. It wQl thus be appreciated that those skilled in the art will be a le to devise numerous systems, apparatus and methods widen, although not explicitly shown or described herein, embody Ihe principles of the invention and are thus within the spirit and scope of the invention as defined by its claims.

For example, the methods and systems described herein could be applied to virtually examine an animal, fish or banimats object Besides me stated uses in the medical field, applications of the technique could be used to detect the contents of sealed objects which cannot be opened. The technique could also be used inside an architectural structure such as a building or cavern and enable the operator to navigate through the structure.

Material which is outside the scope of the claims does not constitute part of the claimed invention. ,898,784 1 2 TRANSFERRING ENCRYPTED PACKETS network packets to the computer on the internal network, the OVER A PUBLIC NETWORK destination computer for each encrypted network packet is determined. Determining the destination computer may This is a continuation of application Sen No. 08/586,230, include determining whether a source computer that sent filed Jan. 16, 1996, now abandoned. 5 each encrypted network packet is authorized to send encrypted network packets to the destination computer.

BACKGROUND Determining the destination computer may also include This invention relates to transferring encrypted packets examining a field in a header of the network packet, and the over a public network. field may correspond to a virtual network tunnel.

Referring to FIG. 1, while executing a variety of software 10 An encrypted network packet may be passed to the applications, 10, 12, 14, for example, Telnet 10 or computer on the internal network if the computer on the Microsoft™, Inc. Word™ 12, computers 16 and 18 may internal network is determined to be the destination com-exchange data over networks 20, 21, for example, a tele- pu'er. Instead, the encrypted network packet may be phone company network, a private network, or a public decrypted at the network interface computer when the network such as the internet or X.25. The applications 15 network interface computer is determined to be the desti-communicate using network protocols 22, 24, 26, for nation computer. Network packets decrypted by the network example, transmission control protocol/internet protocol interface computer may be passed to a computer on an (TCP/IP) 22 or internet packet exchange (IPX) 24, through internal network. application programming interfaces 28, 30, 32. Through The method may also include encrypting network packets application programming interfaces 34, 36, 38, the network 20 and sending the encrypted network packets from the net-protocols communicate with network drivers 40, 42, 44 to work interface computer to the network. The computer on direct network interface hardware 46, 48 to transfer data the internal network may encrypt the network packets, and over the networks. the method may further include passing the encrypted net- While on a network, data being transmitted, including the w"rk packets to the network interface computer. The net-addresses of the source and destination computers 16, 18, is 25 work interface computer may be a firewall computer, and the accessible to others who may be monitoring the network. network may be a public network.

For security, the data is often encrypted before being sent on In general, in another aspect, the invention features the network. receiving encrypted network packets at a first computer over Referring also to FIG. 2, for additional security, firewall 3Q a network from a second computer, and examining a field in computers 16, 18, which have direct access to a network 20 each network packet to determine which of a plurality of may be used to prevent unauthorized access to internal/ encryption algorithms was used to encrypt the network private networks 50, 52. For example, when an internal packet. The network packet is then decrypted in accordance network driver 53 within firewall computer 16 receives data with the determined encryption algorithm, from an internal computer 54 that is destined for a computer 35 Implementations of the invention may include one or 56 on a public network, it encrypts the data and the addresses more of the following features. The field may be examined of source computer 54 and destination computer 56. Com- to determine a destination computer for each encrypted puter 16 then prepends to the encrypted data a new IP header network packet. A determination may be made as to whether including its own address as well as the address of a a source computer that sent each encrypted network packet destination computer, which may also be a firewall ^ is authorized to send encrypted network packets to the computer, e.g., computer 18. destination computer. Encrypted network packets may be When a firewall computer receives a network packet from Passed t0 a computer on an internal network when the the network, it determines whether the transmission is destination computer is determined to be the computer on authorized. If so, the computer examines the header within Ihe internal network. The network packets may be decrypted the packet to determine what encryption algorithm was used 45 when the destination computer is determined to be the first to encrypt the packet. Using this algorithm and a secret key, computer, and the decrypted network packets may be passed the computer decrypts the data and addresses of the source to a computer on an internal network. The field may corre-and destination computers 54, 56 and sends the data to the sPond to a virtual network tunnel, and the network may be destination computer. If both the source and destination * public network. The first computer may be a firewall computers are firewall computers, the only addresses visible 50 computer. (i.e., unencrypted) on the network are those of the firewall In general, in another aspect, the invention features computers. The addresses of computers on the internal receiving network packets over a network, and determining networks, and, hence, the internal network topology, are which virtual tunnel each network packet was sent over is hidden. This has been termed "virtual private networking" made. Each network packet is then routed to a destination (VPN). 55 computer in accordance with the determined virtual tunnel.

Encrypting/decrypting data has been performed by com- Implementations of the invention may include one or plex security software within applications or, to simplify the more of the following features. Each network packet may be applications, encrypting/decrypting has been performed decrypted in accordance with the determined virtual tunnel, within the protocol stack of network protocols. In general, in another aspect, the invention features 60 encrypting network packets at a computer connected to an SUMMARY internal network and passing the network packets over the In general, in one aspect, the invention features a method internal network to a network interface computer. The of handling network packets. Encrypted network packets are network interface computer then passes the encrypted net-received from the network at a network interface computer work packets over a public network, and passed to a computer on an internal network. 65 In general, in another aspect, the invention features Implementations of the invention may include one or receiving network packets from a network, and determining more of the following features. Before passing the encrypted over which virtual tunnel each network packet was sent. A ,898,784 3 4 determination is also made as to whether the source com- Referring to FIGS. 3 and 4, as an example, to send data puter that sent each network packet is authorized to send from computer 16 to computer 18 on the interncl, Telnet 10 network packets over the determined virtual tunnel. issues (step 60) a send call to TCP/IP 22 through network Implementations of the invention may include one or protocol API 28. The send call includes a network packet 62 more of the following features. Each network packet may be 5 (FIG- 5) having a header 64 and data 66. The header includes routed to a destination computer in accordance with the "^formation such as the addresses of the source and desti-detcrmined virtual tunnel when the source computer is nation computers and the type of application that sent the j . _· j . , ■ j data. The network protocol then issues (step 68) a send call determined to be authorized. . .. , . . . , . . . v . ·.. to the network driver API which, in accordance with the Advantages of the invention may include one or more of aitered road maP) issues (step 70) a send call t0 a security the following. Using the policy id field to create virtual 10 network driver (SND) 72. tunnels allows a receiving computer to determine both a sccurity network drivcr (stcp 74) an encapsu- packet's encryption algorithm and where the packet should lale cal] l0 an encapsulate/decapsulale library 76 through an be routed. Multiple tunnels between the same two computers ^ 77 ln one example, the encapsulate/decapsulate library allows packets encrypted with different encryption algo- uses the swIPe IP Sccurity Protocol created by J. Ioannidis rithms to be sent between the same computers. The virtual of Columbia University and M. Blaze of AT&T™, Inc. tunnels permit the cncapsulating/decapsulating and which is described in an Internet Draft dated Dec. 3, 1993 encrypting/decrypting of network packets to be spread and ;nL.urporated by reference. Referring also to FIG. 6, the across multiple computers. Using the tunnel databases, the encapsulate call generates a new network packet 78 in firewall computers may restrict access to particular tunnels accordance with the swIPe protocol. The new packet and, in effect, perform packet filtering for each tunnel. includes a header 80, a swIPe protocol header 82, and data Other advantages and features will become apparent from 84. According to options within the swIPe protocol, header the following description and from the claims. 80 may be the original header 64 (FIG. 5), in which case, DESCRIPTION C' ' 'S ^ °"8'na' ^at "·> or header 80 may be a new header including the address of a source firewall computer, FIG. 1 is a block diagram of two computers connected e.g., computer 16 (FIG. 2), and a destination computer together through two networks. which may also be a firewall computer, e.g., 18. Where FIG. 2 is a block diagram of two firewall computers and header 80 is a new header, data 84 includes the entire networks. original network packet 62 (FIG. 5).

FIG. 3 is a block diagram of a computer including a 30 After encapsulating the network packet, the ^security net-security network driver. work driver issues (step 88, FIG. 4) an encryption call to an ΠΟ. 4 is a flow chart of encapsulation and encryption. encryption/decryption library 90 (FIG.3) through an API 91. „ „„ - , , . i l l - r . 1 1 . Library 90 encrypts a portion 92 of the encapsulated net- FIGS. 5 and 6 are block diagrams of network packets. , J , . . , v.. j . „ . , J t ,„ , . . . . work packet including data 84 and part of swIPe protocol FIG. 7 is a flow chart ot decryption and decapsulation. ^ neader g2 Header 8Q (FIG 6) h not encryp,ed. Thus, jf; FIG. 8 is a block diagram of virtual tunnels. according lo options wilhin the swIPe protocol, header 80 is FIG. 9 is a block diagram of a computer network. the original header 64 (FIG. 5), then the addresses of the FIG. 10 is a flow chart of tunnel record generation. source and destination computers arc visible on the internet. FIG. 11 is a flow chart of tunnel record updating. On the other hand, if header 80 is a new header including the As seen in FIG. 3, security network driver software 72 is 40 addresses of firewall computers, then the addresses of inter-inserted between network protocol TCP/IP 22 and corrc- nal source and destination computers arc encrypted and not sponding network driver 40. The security network driver visible on the internet. encrypts information before it is sent on the network by the Library 90 may be of the type sold by USA Data network drivcr and decrypts information received from the Security™, Inc. of Redwood City, California and may network by the network driver before the information is sent 45 encrypt the data according to an RSA algorithm such as RC2 to the network protocol. As a result, after choosing a security or RC4 or according lo a federal information processing network driver with the required security features, users may standard (FIPS) such as data encryption standard (DES). freely choose among available applications and network The security network driver then issues (step 94) a send protocols regardless of the required level of security and call, including the encapsulated/encrypted network packet, regardless of the available encryption/decryption libraries 50 to the API, and the API, in accordance with the altered road and without having to compromise their security needs. map, issues (step 96) a send call to a network driver, e.g., Moreover, the chosen applications and network protocols network driver 40. The network driver then causes hardware need not be modified. To change the level of security, the 46 to transmit (step 98) the encapsulated/encrypted network user may simply chose another security network driver or packet on the network. modify the current security network driver. 55 Referring to FIGS. 3 and 7, the network drivers of each Generally, a computer's operating system software computer 16, 18 (FIGS. 2 and 3) maintain a database of defines a "road map" indicating which applications may addresses lo which they will respond. For example, when communicate with each other. To insert a security network network driver 40 receives (step 100) a properly addressed driver between a network protocol and a network driver, the network packet from network 20, the network driver issues road map is altered. The vendor of the operating system 60 (step 102) a receive call to corresponding network protocol software may make the road map available or the road map API 34. In accordance with the altered road map, the API may be determined through observation and testing. Once issues (step 104) a receive call to security network driver the road map is altered, functions such as send and receive, (SND) 72 which issues (step 106) an authorization call to between the network protocol and the network driver are encapsulate/decapsulate library 76 through API 77. Library diverted to the network security driver to encrypt data before 65 76 examines the unencrypted portion of swIPe header 82 it is sent on the network and to decrypt data when it is (FIG. 6) to determine (step 108) whether it is proper. If it is received from the network. not proper, an error (step 110) is flagged. ,8! >8,784 6 If the header 82 is not a swIPe header, then the security sends the encrypted packet to internal firewall computer 158 network driver issues a receive call to the API including the in accordance with the tunnel database. unaltered packet. Internal firewall computer 158 also has a tunnel database If the swIPe header is proper, the security network driver in which the internal path of any tunnels connected to issues (step 112) a decryption call to encryption/decryption 5 computer 158 are stored. As a result, when computer 158 library 90 through API 91. A portion of the unencrypted receives a packet whose policy id indicates that it came swIPe protocol header includes a policy identification (id) through a tunnel that ends with computer 158, e.g., tunnel field 113. The policy id field indicates the encryption algo140, it decapsulates and decrypts the packet according to the rithm used to encrypt the data. Library 90 uses a secret key policy id and sends the decrypted packet over internal that was previously exchanged between the computers and 1° network 160 to computer 162 in accordance with the the encryption algorithm to decrypt data 84. decrypted destination address.

After decryption, the security network driver issues (step The only addresses visible on the internet and on internal 114, FIG. 7) a digital signature check call to encapsulate/ network 154 are the addresses of the firewall computers 146, decapsulate library 76. The swIPe protocol header includes 148, and 158. The address of internal computer 162 and, a digital signature 86. The digital signature is a unique 15 hence, the network topology of network 160 are protected on number calculated using the data in the network packet, the both the internet and internal network 154. secret key, and a digital signature algorithm. Library 76 The tunnel databases provide the firewall computers 146, recalculates the digital signature and compares (step 116) it 148, and 158 with information as to the internal path of the to digital signature 86 in the network packet. If the network 20 tunnels. Thus, if computer 162 was another firewall packet is tampered with during transmission and any data computer, computer 146 may modify the destination address within the packet is changed, then the digital signature in the of packets received on tunnel 140 to be the address of packet will not match the digital signature generated by the computer 162 to cause computer 158 to send the packet receiving computer and an error (step 118) will be flagged. directly to computer 162 without checking the policy id If the signatures match, then the security network driver fidd-issues (step 120) a receive call to the API which issues (step Encapsulating/decapsulating and encrypting/decrypting 121) a receive call to the TCP/IP network protocol including network packets may require a large portion of a computer's only the original network packet 62 (FIG. 5, data 66 and processing power. Creating virtual tunnels using the policy addresses of the source and destination computers 64). If id field allows the encapsulating/decapsulating and (step 122) the network packet is destined for computer 16, 30 encrypting/decrypting of network packets to be spread then TCP/IP issues (step 124) a receive call to an application across several computers. For example, computer 146 may 10, 12 and if the network packet is destined for a computer decapsulate and decrypt network packets destined for comon an internal network, e.g., computer 54 (FIG. 2) on puters connected to internal network 154 while computer network 50, then TCP/IP issues (step 126) a receive call to 158 may decapsulate and decrypt network packets destined internal network driver 53 which then sends (step 128) the 35 for computers connected to internal networks 154 and 160. data to the internal computer. Similarly, computer 146 may encapsulate and encrypt net¬ Referring to FIG. 8, the policy id field may be used to work packets sent from computers connected to internal create virtual tunnels 140, 142 between firewall computers network 154 while computer 158 may encapsulate and 146, 148 on internet 152. When computer 146 receives a encrypt network packets sent from computers connected to network packet, it checks the policy id to determine which 40 internal networks 154 and 160. "tunnel" the packet came through. The tunnel indicates the The Kerberos Key Distribution Center components of type of encryption algorithm used to encrypt the packet. Kerberos Network Authentication System created under Multiple tunnels 140, 142 may connect two computers project Athena at Massachusetts Institute of Technology, 146, 148 and each tunnel may use a different encryption defines one method of providing computers with secret keys. algorithm. For example, tunnel 140 may use the RC2 45 Referring to FIG. 9, computer 130 is termed the "trusted" encryption algorithm from RSA Data Security™, Inc. while computer, and before computers 132 and 134 may transfer tunnel 142 uses the F1PS DES encryption algorithm. encrypted data to each other over network 136, both comBecause the RC2 encryption algorithm is less secure and puters send a request to trusted computer 130 for a secret requires less computer processing time than the FIPS DES key. For a more detailed description of the Kerberos Key standard, users may send a larger number of network packets 50 Distribution Center, see RFC1510 (request for comment) requiring less security over tunnel 140 as opposed to tunnel "Kerberos Network Authentication Service" by J. Kohl & B. 142. Similarly, predetermined groups of users or computers Neuman, Sept. 10, 1.993, which is incorporated by reference. may be restricted to sending their packets over particular Referring back to FIG. 2, to transfer secure (i.e., encaptunnels (effectively attaching a packet filter to each tunnel). sulated and/or encrypted) network packets between two The tunnel may also indicate where the packet is to be 55 computers, operators of the two computers may verbally sent. Primary firewall computers 16, 18 store information exchange a secret key for each tunnel between the computabout the internal path of each tunnel in a tunnel database. ers and then manually initialize the computers lo transfer When computer 146 receives a packet whose policy id data by generating a tunnel record including a secret key for indicates that the packet came through a tunnel that ends at each tunnel between the two computers. Firewall computers computer 146, e.g., tunnel 142, computer 146 decapsulates 60 are typically managed by skilled technicians capable of and decrypts the packet and sends the decrypted packet over generating tunnel records. Typical users have non-firewall internal network 154 lo the proper destination computer in computers and may wish to transfer encapsulated/encrypted accordance with the decrypted destination address. When data with a firewall computer. To avoid requiring that a computer 146 receives a packet whose policy id indicates typical user generate tunnel records and instead of having a that it came through a tunnel that does not end with 65 separate trusted computer provide secret keys to two computer 146, e.g., tunnel 140, computer 146 does not computers, a firewall computer 16, 18 may provide secret decapsulate and decrypt the packet. Instead, computer 146 keys to other computers. ,898, 784 7 8 Referring also to FIG. 10, when a user wishes to transfer 3. The method of claim 2, wherein determining a destipackets between his/her computer and a firewall computer, nation computer further includes: the user requests (step 170) a password (a onetime pad) from determining whether a source computer that sent the the firewall operator. The operator then generates (step 172) encrypted network packet is authorized to send tunnel records for each tunnel over which the user's com- 5 encrypted network packets to the destination computer. puter and the firewall computer may transfer network pack4. The method of claim 2, wherein determining a destiets. The operator also stores (step 174) the password given nation computer includes: to the user on the firewall computer. The user installs (step examining an index field in a header of the network 176) the security network driver (SND) software on his/her packet. computer and runs (step 178) a configuration program. The 10 5. The method of claim 4, wherein the field corresponds configuration program prompts (step 180) the user for the to a virtual network tunnel. password and sends (step 182) a configuration request to the 6. The method of claim 2, wherein an encrypted network firewall computer. packet is passed to the computer on the network that is internal with respect to the first computer when the desti¬ The firewall computer identifies (step 184) the user's nation computer for the encrypted network packet is detercomputer as the sender of the request and notifies the user's 15 mined to be the computer on the network that is internal with computer of the available tunnels by sending (step 186) the respect to the first computer. complete tunnel records, including secret keys, associated 7. The method of claim 1, further comprising: with each tunnel to the user's computer. The tunnel records decrypting an encrypted network packet at the first comare sent through network packets that are encrypted using puter when the destination computer for the encrypted the password and the encryption algorithm. Afterwards, the 20 network packet is determined to be the first computer. firewall deletes (step 188) the password, and further network 8. The method of claim 7, further comprising: packets are transmitted between the two computers through passing the decrypted network packet to the computer on the available tunnels and encrypted according to the secret the network that is internal with respect to the first key associated with each tunnel. computer.

Referring to FIG. 11, generally, each time the user's 25 9. The method of claim 1, further comprising: computer accesses (step 190) the internet, a new internet encrypting network packets; and address is assigned. The firewall computer needs to know sending encrypted network packets from the first comthe new address in order to update the tunnel records. To puter to the external network. notify the firewall computer of the new internet address, 10. The method of claim 9, wherein the computer on the each time the user's computer accesses the internet, the 30 network that is internal with respect to the first computer configuration software issues (step 192) a connect request to encrypts the network packets, and further comprising: the firewall computer. The firewall computer identifies (step passing the encrypted network packets to the first com194) the computer and may prompt the user for a user name puter. and a user password. If the user name and password arc 11. he method of claim 1, wherein the first computer authorized (step 196), the firewall updates (step 198) the 35 comprises a firewall computer. tunnel records with the internet address sent as part of the 12. The method of claim 1, wherein the external network connect request. The configuration software also updates comprises a public network. (step 200) the non-firewall computer's tunnel records with 13. A method of handling network packets, comprising the computer's new internet address. receiving an encrypted network packet from a public net¬ Other embodiments are within the scope of the following 40 work at a firewall computer; claims. determining the destination computer of the encrypted For example, instead of encapsulating the network packnetwork packet by examining a virtual tunnel field that ets using the swIPe protocol header, other internet security corresponds to the method of encryption; algorithms may be used. 4J determining whether a source computer that sent the Although the security network driver was described with encrypted network packet is authorized to send respect to send and receive functions, APIs from different encrypted network packets to the destination computer; manufacturers, for example, Sun™, Inc. and Microsoft™, and Inc., include a variety functions, and the security network determining whether to decrypt the encrypted network driver is designed to respond to each possible function. JQ packet at the firewall computer or to pass the encrypted The security network driver may also be simultaneously network packet to a computer on a network that is connected to multiple network protocols, e.g., both TCP/IP internal with respect to the first computer for decryp22 and IPX 24, as shown in FIG. 3. tion.

What is claimed is: 14. A method of handling a network packet, comprising 1. A method of handling network packets, comprising: J5 receiving an encrypted network packet at a first computer receiving an encrypted network packet from an external over a network from a source computer; network at a first computer; and examining a field in the network packet to determine determining whether to decrypt the encrypted network which of a plurality of encryption algorithms was used packet at the first computer or to pass the encrypted to encrypt the network packet and to determine a network packet to a computer on a network that is 60 destination computer for each encrypted network internal with respect to the first computer for decryppacket; and tion. decrypting the network packet at the determined destina¬ 2. The method of claim 1, further comprising, before tion computer. passing the encrypted network packet to the computer on the 15. The method of claim 14, further comprising: network that is internal with respect to the first computer 65 determining whether a source computer that sent each determining a destination computer for the encrypted encrypted network packet is authorized to send network packet. encrypted network packets to the destination computer. ,898, 784 9 10 16. The method of claim 14, further comprising: 24. Λ method of handling a network packet, comprising: passing encrypted network packets to a computer on an encrypting network packets at a first computer connected internal network when the destination computer is to an internal network; determined to be the computer on the internal network. 17. The method of claim 14, further comprising: 5 storing a virtual tunnel identifier in the packet that is used decrypting network packets when the destination comto determine routing of the packet; puter is determined to be the first computer. passing the encrypted network packet over the internal 18. The method of claim 17, further comprising: network to a public network interface computer; and passing the decrypted network packets to a computer on an internal network. passing the encrypted network packet over a public net¬ 19. The method of claim 14, wherein the field corresponds work connected to the public network interface comto a virtual network tunnel. puter.

. The method of claim 14, wherein the network com25. A method of handling network packets, comprising: prises a public network. 15 21. The method of claim 14, wherein the first computer receiving network packets sent over a network at a first comprises a firewall computer. computer; 22. A melhod of handling an encrypted network packet, examining each packet's virtual tunnel field to determine comprising: which virtual tunnel each network packet was sent over receiving the encrypted network packet sent over a net- 2o and whether a source computer that sent each network work at a first computer; packet is authorized to send network packets over the determining which virtual tunnel the network packet was determined virtual tunnel. sent over; and 26. The method of claim 25, further comprising: routing the network packet to a destination computer that is internal with respect to the first computer in accor- 25 routing each network packet to a destination computer in dance with the determined virtual tunnel. accordance with the determined virtual tunnel when the 23. The method of claim 22, further comprising: source computer is determined to be authorized. decrypting each network packet in accordance with the determined virtual tunnel. * * * * *

Claims (3)

39 Claims:

1. A system for performing virtual colonoscopy comprising: an imaging scanner for acquiring image data of a colon a processor, said processor receiving the image data and identifying the interior of the colon, generating a centeriine for navigating through the interior of the colon, detecting a collapsed region of the interior of the colon, and extending the centeriine through the collapsed region a display unit operatively coupled to the processor for displa ing a representation of the colon.

2. Tbe system for perforating virtual colonoscopy according to claim l , wherein the processor detects the collapsed region by calculating entropy values for the intensity values of the image data proximate the centeriine and identifying at least one of the entropy values being above a threshold value.

3. The system for performing virtual colonoscopy according to claim l , wherein the processor virtually expands a detected collapsed region of the colon in accordance with properties of the colon wall