KR20180115122A - Image processing apparatus and method for generating virtual x-ray image - Google Patents

Abstract

According to an embodiment of the present invention, disclosed is a virtual X-ray image generating method for quickly generating a virtual X-ray image from a three-dimensional image which comprises the steps of: extracting a radius size and feature points of a blood vessel in a three-dimensional image on an object; projecting the extracted feature points onto a two-dimensional plane; determining a brightness value of pixels positioned within a predetermined distance range for each of the feature points projected onto the two-dimensional plane; and generating a virtual X-ray image by coloring each pixel with the determined brightness value.

Description

[0001] IMAGE PROCESSING APPARATUS AND METHOD FOR GENERATING VIRTUAL X-RAY IMAGE [0002]

The present invention relates to the field of image processing. More particularly, the present invention relates to a method and apparatus for generating a virtual x-ray image from a three-dimensional image of a subject.

In general, vascular intervention is performed by inserting a needle or a catheter percutaneously into an intravascular lesion based on a fluoroscopic image obtained using an X-ray imaging apparatus without a separate surgical incision, And the like. In addition, since the x-ray image device can easily grasp the internal structure of the object, it is used in the medical field to detect an abnormality such as a lesion inside the human body or to grasp the internal structure of an object or a part.

In the case of conventional vascular intervention, it is not easy to locate the three-dimensional shape of the blood vessel and the position of the surgical tool because it refers only to the two-dimensional x-ray image lacking depth information. If the catheter advances to the wrong path, There is also a risk of occurrence. In addition, it is very difficult to determine the direction of the catheter in areas where the contrast can not be reached due to vascular occlusion and can not be visualized on the x-ray image. As a result, the accuracy and stability of the procedure tends to be highly dependent on the experience or proficiency of the operator .

An image processing apparatus and a virtual x-ray image generating method according to an embodiment of the present invention aim to quickly generate a virtual x-ray image from a three-dimensional image.

Further, the image processing apparatus and the virtual x-ray image generating method according to an embodiment of the present invention improve the matching speed of the three-dimensional image and the actual x-ray image, thereby increasing the accuracy of the procedure, .

A method of generating a virtual x-ray image according to an exemplary embodiment of the present invention includes:

Extracting characteristic points and radius magnitudes of the blood vessel in the three-dimensional image with respect to the object; Projecting the extracted feature points onto a two-dimensional plane; Determining a brightness value of pixels located within a predetermined distance range for each feature point projected on the two-dimensional plane; And generating a virtual x-ray image by coloring each pixel with the determined brightness value.

The step of projecting the extracted feature points onto a two-dimensional plane may include projecting the extracted feature points on the two-dimensional plane in consideration of a conversion relationship between the reference coordinates of the three-dimensional image and the reference coordinates of the two- .

Wherein the conversion relationship between the reference coordinates of the three-dimensional image and the reference coordinates of the two-dimensional plane is determined by an internal parameter indicating a conversion relation between the reference coordinates of the x- And an external parameter indicating a conversion relation between the reference coordinates of the patient or the phantom.

The virtual x-ray image generating method includes: matching the generated virtual x-ray image with an actual x-ray image of a target object photographed by the x-ray apparatus; And if the similarity of the virtual x-ray image and the actual x-ray image is less than a predetermined standard as a result of the matching, updating the external parameter to reproduce the virtual x-ray image.

The step of determining the brightness value of the pixels may include selecting pixels within a predetermined distance range corresponding to the extracted radius size for each feature point centered at each feature point projected on the two dimensional plane have.

The step of determining the brightness value of the pixels may include determining the brightness value of the selected pixels corresponding to each feature point to be lower as the radius corresponding to each feature point is larger.

Wherein the step of determining the brightness value of the pixels comprises the step of determining if any one of the pixels is located within a predetermined distance range of the first feature point and within a predetermined distance range of the second feature point, And determining a final brightness value of any one of the pixels by superposing a first brightness value determined for the first feature point and a second brightness value determined for the one of the pixels corresponding to the second feature point .

The step of determining the brightness value may include the step of determining a brightness value according to Equation 1,

[Equation 1]

In the above Equation 1, j is an index of each feature point, x is a coordinate of each pixel included in the two-dimensional plane, I _j (x) is a brightness value set for the pixel x with respect to the feature point j, , r _j is the radius size corresponding to the feature point j, and C _j ^2D is the coordinate in the two-dimensional plane of the feature point j.

The final brightness value of each pixel included in the two-dimensional plane is determined according to Equation (2) below,

&Quot; (2) "

In Equation 2, I (x) is the final brightness value of the pixel x, N is the number of the feature points projected onto the two-dimensional plane, and I _j (x) .

The step of extracting the radius size and feature points of the blood vessel may include: binarizing the three-dimensional image based on a predetermined threshold value; Extracting center points of the blood vessel in the three-dimensional image as the minutiae points through a skeletonization algorithm; And extracting a radius size corresponding to each of the center points of the blood vessels in the three-dimensional image through a Hessian analysis algorithm.

An image processing apparatus according to another embodiment of the present invention includes:

An image analyzer for extracting characteristic points and a radius size of a blood vessel in a three-dimensional image of the object; And projecting the extracted feature points on a two-dimensional plane, determining brightness values of pixels located within a predetermined range for each feature point projected on the two-dimensional plane, coloring each pixel with the determined brightness value, And an image generating unit for generating an image.

The image processing apparatus and the virtual x-ray image generating method according to an embodiment of the present invention can quickly generate a virtual x-ray image from a three-dimensional image.

In addition, the image processing apparatus and the virtual x-ray image generating method according to the embodiment of the present invention can improve the matching speed and the matching accuracy of the three-dimensional image and the actual x-ray image.

In addition, the image processing apparatus and the virtual x-ray image generating method according to the embodiment of the present invention can increase the accuracy of the procedure such as the blood vessel intervention and reduce the load applied to the medical staff according to the long time procedure.

However, the effects of the image processing apparatus and the virtual x-ray image generating method according to an embodiment of the present invention are not limited to those described above, Other advantages will become apparent to those skilled in the art from the following description.

FIG. 1 is a diagram for explaining a general method of matching a virtual x-ray image generated from a three-dimensional image and an actual x-ray image.
2 is an exemplary diagram showing an environment in which an image processing apparatus according to an embodiment of the present invention is applied.
FIG. 3 is a schematic view for explaining a process of generating a virtual X-ray image according to an embodiment of the present invention.
4 is a flowchart illustrating a method of generating a virtual x-ray image according to an embodiment of the present invention.
5 is a view for explaining feature points and radius sizes extracted from a blood vessel in a three-dimensional image.
6 is an exemplary diagram showing a feature point extracted from a blood vessel and projected on a two-dimensional plane.
7 is a diagram for explaining an intrinsic parameter and an extrinsic parameter of the X-ray apparatus.
FIG. 8 is a diagram for explaining a method of determining brightness values of pixels in a two-dimensional plane.
9 is a diagram showing a state in which pixels around the minutiae projected on a two-dimensional plane are colored.
10 is a convex diagram showing a configuration of an image processing apparatus according to an embodiment of the present invention.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. It is to be understood, however, that the intention is not to limit the invention to the specific embodiments, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings. In addition, numerals (e.g., first, second, etc.) used in the description of the present invention are merely an identifier for distinguishing one component from another.

Also, in this specification, when an element is referred to as being "connected" or "connected" with another element, the element may be directly connected or directly connected to the other element, It should be understood that, unless an opposite description is present, it may be connected or connected via another element in the middle.

In the present specification, a component represented by 'unit', 'module', or the like refers to a case where two or more components are combined into one component, or one component is divided into two or more ≪ / RTI > In addition, each of the components to be described below may additionally perform some or all of the functions of the other components in addition to the main functions of the component itself, and some of the main functions And may be performed entirely by components.

Also, in this specification, an 'object' may include a person or an animal, or a part of a person or an animal. For example, the subject may include a liver, a heart, a uterus, a brain, a breast, an organ such as the abdomen, or a blood vessel.

In this specification, a two-dimensional x-ray image generated from a three-dimensional image is referred to as a 'virtual x-ray image', and a two-dimensional x-ray image generated from a detector according to an x- .

Hereinafter, exemplary embodiments according to the technical idea of the present invention will be described with reference to the drawings.

Various methods have been proposed for matching three-dimensional images to x-ray images for conventional vascular intervention procedures. For example, there are a marker-based method, a tracker-based method, and an image-based method. Typically, an image-based method can be classified into a feature-based method and a contrast-based method.

The feature point-based method extracts feature points from each of three-dimensional images and actual x-ray images, and matches the three-dimensional images to the actual x-ray images through feature point matching. Although the feature point based method has an advantage that the matching speed is fast, there is a disadvantage that the accuracy is relatively low.

As shown in FIG. 1, the contrast-based method is a method in which a virtual X-ray source 4 assumes a case where a virtual X-ray source 2 radiates a virtual X-ray 2 into a CT image 3 as a 3D image, Dimensional image 3 is matched with the actual x-ray image 5, and then the three-dimensional image 3 is matched to the actual x-ray image 5.

The contrast-based method has a merit that it has higher accuracy than the feature point-based method, but it takes a long time to generate a virtual x-ray image (4). Specifically, a virtual x-ray image 4 is generated in consideration of the extent to which the virtual x-ray 2 emitted by the virtual x-ray source 1 is attenuated by each voxel of the three-dimensional image 3, It takes a long time to generate the virtual x-ray image 4. If the image registration takes a long time, confirmation of real-time information of the blood vessel can reduce the accuracy of the procedure in an important procedure.

The virtual x-ray image generation method according to an embodiment of the present invention can provide quick and accurate matching results using both the conventional feature point based method and the contrast based method.

2 is an exemplary diagram showing an environment in which an image processing apparatus 100 according to an embodiment of the present invention is applied.

The image processing apparatus 100 according to an embodiment of the present invention may be implemented as a general-purpose computer as an example. The image processing apparatus 100 can receive a three-dimensional image of the object 10 photographed by the three-dimensional imaging apparatus 200 from the three-dimensional imaging apparatus 200 or receive it from another server.

The three-dimensional imaging apparatus 200 may include a computed tomographic angiography (CTA) apparatus. However, the present invention is not limited thereto. If a three-dimensional tomographic image of the object 10 can be obtained, It is possible.

The object 10 can be subjected to a blood vessel intervention in the imaging region of the X-ray apparatus 300, that is, in the region between the X-ray source 310 and the detector 350. At this time, A virtual x-ray image for matching an actual x-ray image to be photographed against a three-dimensional image can be generated.

According to an embodiment of the present invention, by matching a local model (for example, a three-dimensional blood vessel model to be treated) in a three-dimensional image or a three-dimensional image to an actual x- The three-dimensional structure of blood vessels can be more clearly shown to the medical staff.

The X-ray apparatus 300 shown in FIG. 2 is a movable C-arm type X-ray apparatus in which an X-ray source 310 emits an X-ray to a target body 10 and a detector 350 transmits the object 10 And acquires a two-dimensional image of the object 10 by detecting the received X-rays. The C-arm type X-ray apparatus of FIG. 2 is only one example, and in one embodiment of the present invention, a fixed X-ray apparatus other than the C-arm type X-ray apparatus may be used.

FIG. 3 is a schematic view for explaining a process of generating a virtual X-ray image according to an embodiment of the present invention.

As described above, an embodiment of the present invention uses both a feature-based method and a contrast-based method. 3, in one embodiment of the present invention, the feature points 411 are extracted from the three-dimensional image 410, the extracted feature points 411 are projected onto the two-dimensional plane 430, Digitally reconstructed radiography (DRR) 450. Then, the virtual x-ray image 450 and the actual x-ray image 470 are matched in a contrast-based manner.

That is, in the case of generating the virtual x-ray image 450, the feature point based method is used, and in the case of image matching, the contrast based method is used, so that quick and accurate image matching can be achieved as compared with the conventional methods.

Hereinafter, an embodiment of the present invention will be described in detail with reference to FIG. 4 and the following figures.

4 is a flowchart illustrating a method of generating a virtual x-ray image according to an embodiment of the present invention.

In step S410, the image processing apparatus 100 extracts the feature points and the radius size of the blood vessel from the blood vessels in the three-dimensional image generated by the three-dimensional imaging apparatus 200. [ For example, when the a feature point and the b feature point are extracted from the blood vessel, the radius of the a corresponding to the a feature point and the radius of the b corresponding to the b feature point are respectively Can be extracted.

5 is a diagram for explaining feature points and radius magnitudes extracted from the blood vessel 415 in the three-dimensional image 410. As shown in FIG. 5, the image processing apparatus 100 includes feature points in blood vessels identified in a three- Radius size can be extracted. C _j ^3D in Fig. 5 represents the coordinates of the feature point j based on the reference coordinates of the three-dimensional image, and R _j represents the radius vector of the feature point j.

The image processing apparatus 100 can extract a feature point and a radius size from a blood vessel by applying various image processing algorithms. For example, the image processing apparatus 100 binarizes a three-dimensional image based on a predetermined brightness threshold, that is, changes the original brightness value of each voxel to a maximum brightness value or a minimum brightness value, and performs skeletonization, Through the algorithm, the center points of the blood vessels in the three-dimensional image can be extracted as respective feature points. Also, the image processing apparatus 100 may extract a radius size corresponding to each of the center points of the blood vessels in the three-dimensional image by applying a Hessian analysis algorithm.

The thinning algorithm recursively removes pixels that satisfy certain conditions by inputting the binarized image. For a specific condition, the number of neighboring pixels, the value of surrounding pixels, the relative position of neighboring pixels having 0 or 1, and the like are taken into consideration based on the pixels to be removed, and in each case, whether to remove the pixels to be removed is determined . When the thinning algorithm is repeatedly performed, the center line of the blood vessel having a thickness of 1 pixel can be obtained.

The Hessian analysis is to obtain the radius of the blood vessel corresponding to the center of the blood vessel. The second derivative Gaussian normal distribution is used as a kernel to obtain the largest response value by calculating the convolution from various variance values. Method. When the second derivative Gaussian normal distribution is used as the kernel, the composite product is performed, and the thickness of the line can be determined according to the variance value of the normal distribution. By performing the composite product for the plurality of variance values and selecting the variance having the largest response value, The size of the radius, that is, the radius.

Referring again to FIG. 4, in step S420, the image processing apparatus 100 projects the feature points extracted from the blood vessel on a two-dimensional plane. Here, projecting the feature points may refer to a process of checking where each of the feature points of the three-dimensional image is mapped to the two-dimensional plane.

For example, as shown in FIG. 6, the three-dimensional coordinate values C ^3D of each feature point are converted into coordinate values C ^2D in the two-dimensional plane 430 and projected onto the two-dimensional plane 430 The image processing apparatus 100 can project the intravascular feature points onto the two-dimensional plane 430 in consideration of the conversion relationship between the reference coordinates of the three-dimensional image 415 and the reference coordinates of the two-dimensional plane 430 have.

The method of projecting the minutiae points will be described in detail with reference to Fig.

The object 10 is located in the imaging area of the x-ray apparatus 300, that is, in the area between the x-ray source 310 and the detector 350. In this case, in order to generate a virtual x-ray image similar to an actual x-ray image generated by the detector 350 by radiating the x-ray by the x-ray source 310, The internal parameter I and the external parameter E are acquired and used to calculate the conversion relationship between the reference coordinates of the three-dimensional image and the reference coordinates of the two-dimensional plane.

7, the inner product parameter I corresponds to a transformation relationship between the reference coordinate (a) of the x-ray source 310 and the reference coordinate (c) of the detector 350. The arrow corresponding to the inner product parameter I in FIG. 7 means that the inner product parameter I contains the relative movement information of the x-ray source 310 with respect to the detector 350. The inner product parameter (I) can be expressed as the following matrix.

[Internal parameter]

In the inner product parameter I,? X and? Y mean focal lengths corresponding to the vertical distance between the x-ray source 310 and the detector 350,? X being the focal length of the pixels of the detector 350, And? Y denotes a value obtained by dividing the focal distance by the length of the other side of the lengthwise side of the pixel of the detector 350 and the length of the other side of the lateral side. X0 and y0 are the image main points representing the offset distance between the center point of the x-ray source 310 and the center point of the detector 350 and s is the distance between the central point of the detector 350 and the central point of the detector 350, Which is the slope between horizontal and vertical sides.

7, the external parameter E is a transformation between the reference coordinate (a) of the x-ray source 310 and the reference coordinate (b) of the object 10 located in the imaging area of the x- Relationship. The arrow corresponding to the external parameter E in Fig. 7 means that the external parameter E includes the relative movement and rotation information of the object 10 with respect to the x-ray source 310. [ The external parameter E can be expressed by the following matrix.

[External parameter]

In the matrix of the external parameter (E), r represents the elements related to the rotation angle, and t represents elements related to the movement distance.

Since the subject 10 can continue to move during the procedure, a phantom (not shown) is placed in the imaging area to calculate the inner and outer parameters I and E, (I) and the external parameter (E).

For example, when the coordinates of the minutiae points of the phantom based on the phantom reference coordinates are (X, Y, X) and the coordinates of the same minutiae in the two-dimensional phantom image based on the reference coordinates (c) When the coordinate is (u, v), the relationship between these two coordinates may correspond to the following equation (1).

[Equation 1]

In Equation (1), w is a distance weight, and P is a 3x4 projection matrix. If P is obtained through a single value decomposition (SVD) algorithm according to coordinate values of previously known feature points, an inner parameter I and an outer parameter E can be obtained by QR decomposition of the projection matrix P .

In an embodiment of the present invention, the image processing apparatus 100 may acquire the internal parameter (I) and the external parameter (E) based on the x-ray image obtained by radiographing the phantom with the phantom placed in the imaging area Or the inner product parameter I and the outer product parameter E may be calculated in advance and stored in the image processing apparatus 100. [

The image processing apparatus 100 can calculate the transformation relation (T) between the reference coordinates of the three-dimensional image and the reference coordinates of the two-dimensional plane by the following equation (2).

&Quot; (2) "

T = I · E

The transformation relation T means a transformation relation between the reference coordinate b of the object 10 and the reference coordinate c of the detector 350. The image processing apparatus 100 determines the transformation relation T as It can be used as a conversion relation between the reference coordinates of the three-dimensional image and the reference coordinates of the two-dimensional plane.

The image processing apparatus 100 can calculate the coordinates in the two-dimensional plane of each of the minutiae points by applying the coordinates of the minutiae points of the three-dimensional blood vessel to the conversion relation (T) of Equation (2).

Referring again to FIG. 4, in step S430, the image processing apparatus 100 determines a brightness value of pixels positioned within a predetermined range for each feature point projected on a two-dimensional plane.

Since the two-dimensional plane acts as a kind of virtual detector, it can be composed of a plurality of pixels like the detector 350 (see pixel 431 in FIG. 6). The image processing apparatus 100 can determine the brightness value of pixels around each feature point based on the radius size corresponding to each of the feature points projected on the two-dimensional plane.

In brief, the image processing apparatus 100 selects pixels positioned around each of the minutiae according to the radius magnitude corresponding to each of the minutiae projected on the two-dimensional plane, and sets the brightness value of the selected pixels in inverse proportion to the radius magnitude You can decide. This is because, in the actual x-ray image, the brightness value of the thick blood vessel is darker than the brightness value of the thin blood vessel.

This will be described in detail with reference to FIG.

FIG. 8 is a diagram for explaining a method of determining brightness values of pixels of the two-dimensional plane 430. FIG.

Assuming that the first feature point 810a, the second feature point 810b and the third feature point 810c are projected in the two-dimensional plane 430 as shown in FIG. 8, first, the image processing apparatus 100 Selects the first group 820a as the center of the first feature 810a and the second feature 810b as the center of the second feature 810b, 810b as the second group 820b and the pixels located within the radial dimension corresponding to the third feature point 810c around the third feature point 810c as the third group 810c, (820c).

As shown, in the first group 820a and the second group 820b, there may be pixels 830a belonging to both the first group 820a and the second group 820b, 820b and the third group 820c may have pixels 830b belonging to both the second group 820b and the third group 820c.

Next, the image processing apparatus 100 sets the brightness value in inverse proportion to the radius size corresponding to the first feature point 810a to the brightness value for the first group 820a, and sets the brightness value corresponding to the second feature point 810b The brightness value inversely proportional to the radius size is set as the brightness value for the second group 820b and the brightness value in inverse proportion to the radius size corresponding to the third feature point 810c is set as the brightness value for the third group 820c Setting.

The final brightness value of the pixels 830a belonging to both the first group 820a and the second group 820b is set to correspond to the brightness value set corresponding to the first feature point 810a and the brightness value set corresponding to the second feature point 810b Brightness values have overlapping values. Accordingly, the final brightness of the pixels 830a belonging to both the first group 820a and the second group 820b is determined by the brightness of the pixels belonging only to the first group 820a or the brightness of the pixels belonging only to the second group 820b It becomes darker than brightness.

Similarly, the final brightness value of the pixels 830b belonging to both the second group 820b and the third group 820c corresponds to the brightness value set corresponding to the second feature point 810b and the brightness value corresponding to the third feature point 810c And the set brightness values have overlapping values.

As the distance between the minutiae projected on the two-dimensional plane 430 is closer, the neighboring pixels can belong to several groups. Here, the distance between the minutiae points is close to the three-dimensional image when viewed from the projection direction of the three- It may mean that the blood vessel in the image is positioned parallel or almost parallel to the projection direction. That is, a blood vessel positioned parallel to the projection direction will be projected in a very short length on a two-dimensional plane even if its length is very long on a three-dimensional plane.

In the case of an actual x-ray image, a blood vessel positioned parallel to the radiating direction of the x-ray appears dark in the x-ray image. Therefore, in an embodiment of the present invention, a blood vessel positioned parallel to the projection direction of the three- It is necessary to set the brightness values of the pixels around the minutiae points which are very close to each other in the two-dimensional plane to be darker than other pixels. Referring to FIG. 9, it can be seen that blood vessels located parallel to the projection direction are darkened in a two-dimensional plane.

The image processing apparatus 100 can select surrounding pixels of each feature point projected on a two-dimensional plane based on Equation (3) below.

&Quot; (3) "

In Equation (3), j is an index of each feature point, x is a coordinate of each pixel included in the two-dimensional plane, I _j (x) is a brightness value set for the pixel x with respect to the feature point j, , r _j is the radius size corresponding to the feature point j, and C _j ^2D is the coordinate in the two-dimensional plane of the feature point j.

In Equation (3), it can be seen that the pixels included in the predetermined distance range corresponding to the radius size are selected as the pixels corresponding to the respective feature points, with each feature point as a center.

Further, the image processing apparatus 100 can set the final brightness value of the pixels in the two-dimensional plane to the following equation (4).

&Quot; (4) "

In Equation (4), I (x) represents the final brightness value of the pixel x, N represents the number of the feature points projected on the two-dimensional plane, and I _j (x) represents the brightness value set for the pixel x with respect to the feature point j.

Referring to Equation (4), it can be seen that, when any one of the pixels is selected corresponding to a plurality of feature points, the brightness value of the corresponding pixel is superimposedly darkened.

Referring again to FIG. 4, in step S440, the image processing apparatus 100 generates a virtual x-ray image by coloring the pixels included in the two-dimensional plane to a final brightness value.

The image processing apparatus 100 matches the generated virtual x-ray image with the actual x-ray image taken with respect to the object 10, and then determines whether the similarity of the virtual x-ray image and the actual x- Dimensional image and the reference coordinates of the two-dimensional plane are transformed as well, so as to transform the virtual coordinate of the reference coordinate of the three-dimensional image and the reference coordinate of the two- X-ray image can be regenerated.

That is, the image processing apparatus 100 continuously updates the external parameter E until the virtual x-ray image becomes identical to the actual x-ray image or becomes very close to each other, and repeats the generation and matching process of the virtual x-ray image can do.

When the degree of similarity between the virtual x-ray image and the actual x-ray image is equal to or greater than a certain standard, the image processing apparatus 100 knows the projection direction of the three-dimensional image, A local model of the image (e.g., some blood vessels) can be matched to the actual x-ray image and displayed on the screen.

Fig. 10 is a convex diagram showing a configuration of an image processing apparatus 100 according to an embodiment of the present invention.

Referring to FIG. 10, an image processing apparatus 100 according to an exemplary embodiment of the present invention may include a memory 110, a communication unit 130, an image analysis unit 150, and an image generation unit 170. The memory 110, the communication unit 130, the image analysis unit 150, and the image generation unit 170 may be implemented by at least one processor and may operate according to programs stored in the memory 110. [

The memory 110 may store an application for image processing and creation, and may also store a three-dimensional image and an actual x-ray image of the object 10.

The communication unit 130 may receive the three-dimensional image and the actual x-ray image of the object 10 by communicating with the three-dimensional imaging apparatus 200, the x-ray apparatus 300, or other servers.

The image analysis unit 150 extracts the radius magnitude and the characteristic points of the blood vessel included in the three-dimensional image with respect to the object 10.

The image generating unit 170 projects the extracted feature points on a two-dimensional plane, determines brightness values of pixels located within a predetermined range for each of the feature points projected on the two-dimensional plane, You can create a virtual x-ray image by coloring.

Although not shown in FIG. 10, the image processing apparatus 100 may further include a display for displaying a virtual X-ray image on the screen.

Further, if it is determined that the virtual x-ray image and the actual x-ray image are similar to each other, the image processing apparatus 100 knows the projection direction of the three-dimensional image, A local model of the image (e.g., some blood vessels) may be matched to the actual x-ray image and displayed on the screen.

Meanwhile, the embodiments of the present invention described above can be written in a program that can be executed in a computer, and the created program can be stored in a medium.

The medium may include, but is not limited to, storage media such as magnetic storage media (e.g., ROM, floppy disks, hard disks, etc.), optical reading media (e.g., CD ROMs,

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present invention as defined by the following claims. You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

100: Image processing device
110: Memory
130:
150: Image analysis section
170:
200: 3D imaging device
300: X-ray device
310: X-ray source
350: Detector

Claims (12)

Extracting characteristic points and radius magnitudes of the blood vessel in the three-dimensional image with respect to the object;
Projecting the extracted feature points onto a two-dimensional plane;
Determining a brightness value of pixels located within a predetermined distance range for each feature point projected on the two-dimensional plane; And
And generating a virtual x-ray image by coloring each pixel with the determined brightness value. ≪ RTI ID = 0.0 > 11. < / RTI >
The method according to claim 1,
The step of projecting the extracted feature points onto a two-
And projecting the extracted feature points on the two-dimensional plane in consideration of a conversion relation between the reference coordinates of the three-dimensional image and the reference coordinates of the two-dimensional plane.
3. The method of claim 2,
Wherein the conversion relationship between the reference coordinates of the three-dimensional image and the reference coordinates of the two-
An internal parameter indicating a conversion relation between the reference coordinates of the x-ray source of the x-ray source and the detector and the external parameter indicating the conversion relationship between the reference coordinates of the x-ray source and the reference coordinates of the patient or phantom A method of generating a virtual x-ray image.
The method of claim 3,
The virtual X-ray image generation method includes:
Matching the generated virtual x-ray image with an actual x-ray image of the object photographed by the x-ray apparatus; And
Further comprising the step of renewing the virtual parameter x-ray image by updating the parameter if the similarity between the virtual x-ray image and the actual x-ray image is less than a predetermined standard as a result of the matching, .
The method according to claim 1,
Wherein determining the brightness value of the pixels comprises:
And selecting pixels within a predetermined distance range corresponding to the extracted radius size for each feature point centering on each feature point projected on the two-dimensional plane.
6. The method of claim 5,
Wherein determining the brightness value of the pixels comprises:
And determining a lower brightness value of the selected pixels corresponding to each feature point as the radius corresponding to each feature point is larger.
The method according to claim 6,
Wherein determining the brightness value of the pixels comprises:
When a pixel is located within a predetermined distance range of the first feature point and within a predetermined distance range of the second feature point, a first brightness value determined for any one of the pixels corresponding to the first feature point, And determining a final brightness value of any one of the pixels by superposing a second brightness value determined for any one of the pixels corresponding to the two feature points.
The method according to claim 1,
The step of determining the brightness value comprises:
Determining a brightness value according to Equation (1) below,
[Equation 1]

In the above Equation 1, j is an index of each feature point, x is a coordinate of each pixel included in the two-dimensional plane, I _j (x) is a brightness value set for the pixel x with respect to the feature point j, , r _j denotes a radius size corresponding to the feature point j, and C _j ^2D denotes a coordinate in the two-dimensional plane of the feature point j.
9. The method of claim 8,
The final brightness value of each pixel included in the two-dimensional plane is determined according to Equation (2) below,
&Quot; (2) "

In Equation (2), I (x) represents the final brightness value of the pixel x, N represents the number of the feature points projected onto the two-dimensional plane, and I _j (x) represents the brightness value set for the pixel x with respect to the feature point j A virtual x-ray image generation method.
The method according to claim 1,
Wherein the step of extracting the radius size and the feature points of the blood vessel comprises:
Binarizing the three-dimensional image based on a predetermined threshold value;
Extracting center points of the blood vessel in the three-dimensional image as the minutiae points through a skeletonization algorithm; And
And extracting a radius size corresponding to each of the center points of the blood vessels in the three-dimensional image through a Hessian analysis algorithm.
A program stored in a medium for executing the virtual x-ray image generation method of claim 1 in combination with hardware.
An image analyzer for extracting characteristic points and a radius size of a blood vessel in a three-dimensional image of the object; And
Dimensional plane, determining brightness values of pixels located within a predetermined range for each feature point projected on the two-dimensional plane, and coloring each pixel with the determined brightness value to generate a virtual x-ray image And an image generating unit for generating the image data.

Images