JP2006102508A - Method and system for detecting anatomical shape in computer aided detection system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and a system for examining colonography and computer tomographic data obtained from a lung testing. <P>SOLUTION: This invention discloses a method for detecting an anatomical object of interest from image data 82 by computer support. This method includes a step of selecting a plurality of possible candidates 86 of objects of interest from the image data 82 on the basis of a three-dimensional candidate generator, wherein the three-dimensional candidate generator includes a curvature tensor. Further, this method includes a step of classifying a plurality of possible candidates 86 of an object of interest to generate a set of objects of interest reduced in false positive rate. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates generally to methods and apparatus for reviewing medical imaging examinations, and more particularly to methods and apparatus for examination of computed tomography data as obtained from colonography and lung examinations.

  Lung cancer is one of the world's leading causes of death. Typically, chest radiographs have been used as a diagnostic tool for the purpose of detecting human lung disease. Computed tomography (CT) imaging has also been used to facilitate early detection and treatment of lung cancer. CT imaging advantageously provides a highly detailed anatomical depiction, and as a result is increasingly being used to detect and track the development of lesions that can lead to potential cancer.

  Recently, radiologists use advanced lung analysis (ALA) and computed tomography colonography (CTC) techniques to locate lung and colon nodules and polyps. The radiologist detects lung nodules by observing an axial slice of the chest. However, CT systems typically provide several images in a single CT scan. As a result, a significant amount of information is presented to the radiologist for use in reading the image and detecting suspicious areas that may indicate disease. The large amount of data associated with a single CT scan presents a time consuming process to the radiologist. Furthermore, since it is difficult to identify suspicious areas in a wide amount of data, this large amount of data can unfortunately lead to missed cancer detection.

  Colon cancer is one of the leading causes of cancer death, but can be largely avoided by timely detection and removal of precursor colon polyps. CTC has recently been used for early detection of colon polyps. Again, however, interpretation of CTC examination data by a radiologist may be time consuming due to the large number of possible axial CT slices. In addition, the burden of image data for interpretation may be large, resulting in oversight errors.

  Variously described as computer-aided detection, or computer-aided detection or computer-aided diagnosis, a technique often referred to as “CAD” in acronym, has helped radiologists in detecting lung nodules on chest radiographs and chest CT scans It has emerged as a viable method to assist and to detect and diagnose other anatomical structures and pathologies. However, one of the serious and complex problems faced by CAD methods is the initial selection of potential nodule candidates.

Usually, most known CAD methods use a threshold method for initial identification of nodule candidates. However, conventional methods for thresholding CT images face problems associated with certain nodules that may have very low CT values and contrasts that make the proper selection of the thresholds extremely difficult, thereby increasing the number of Radiologists need more time to create false positives, eliminate them, and focus on the actual risks. In addition, many nodules may show the same CT value as blood vessels and tracheal walls, and the blood vessels and tracheal walls are inconvenient because they include blood vessels and tracheal walls as nodule candidates when detecting nodules. Can result in a substantial increase in the number of false positives.
US Pat. No. 6,748,044

  Accordingly, the development of robust techniques and systems that process image data that advantageously facilitates the detection of the majority of nodules on a substantially good initial shape basis while at the same time keeping the number of false positives to a minimum. Can be desirable. Specifically, there is a need for a system that can generate or recognize more realistic candidates for subsequent computer-aided analysis and final examination by qualified medical personnel.

  Briefly, according to one aspect of the present technique, a method for computer-aided detection of an anatomical object of interest from image data is presented. The method includes selecting potential candidates for a plurality of objects of interest based on a 3D candidate generator from image data, wherein the 3D candidate generator includes a curvature tensor. The method further includes classifying a plurality of possible candidates for the object of interest to generate a set of objects of interest with a reduced false positive rate. A computer readable medium that provides functionality in the form defined by this method is also provided by the present technique.

  According to another aspect of the present technique, a system for detecting anatomical objects of interest from image data with computer assistance is presented. The system includes a computer system operably coupled to either a CT image acquisition system or a CT image storage system, wherein the computer system includes a 3D candidate generator, the 3D candidate generator being an image. A three-dimensional candidate generator is configured to select potential candidates for a plurality of objects of interest from the data, and the three-dimensional candidate generator includes a curvature tensor.

  According to yet another aspect of the present technique, a system for detecting anatomical objects of interest from image data with computer assistance is presented. The system includes an x-ray source configured to emit a stream of radiation. The system further includes a region detector configured to detect the radiation stream and generate one or more signals in response to the radiation stream, the detector comprising a plurality of detector elements. Including. The system is also a system configured to rotate the x-ray source and the area detector to collect one or more sets of projection data from one or more of the detector elements by the data acquisition system. Includes controller. In addition, the system includes a computer system operably coupled to either a CT image acquisition system or a CT image storage system, wherein the computer system includes a 3D candidate generator, the 3D candidate The generator is configured to select potential candidates for a plurality of objects of interest from the image data, the three-dimensional candidate generator including a curvature tensor.

  These and other features, aspects and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings. In the drawings, like reference numerals designate like elements throughout the drawings.

  Lung cancer is one of the world's leading causes of death. Typically, chest radiographs have been used as a diagnostic tool for the purpose of detecting human lung disease. Computed tomography (CT) imaging has also been used to facilitate early detection and treatment of lung cancer. Colon cancer is one of the leading causes of cancer death, but can be largely avoided by detecting and removing precursor colon polyps in a timely manner. CTC is currently used for early detection of colon polyps. However, interpretation of CT examination data by a radiologist can be time consuming due to the large number of axial CT slices in some cases. In addition, the overload of image data for interpretation may be excessively overlooked. Computer-aided detection (CAD) has emerged as a viable method to assist radiologists in detecting lung nodules on chest radiographs and chest CT scans. However, one important and complex problem faced by CAD techniques is the initial selection of potential nodule candidates. The techniques discussed herein address some or all of these issues.

  FIG. 1 is a block diagram illustrating an imaging system 10 that collects and processes image data in accordance with the present technique. In the illustrated embodiment, the system 10 is a computer tomography designed to collect X-ray projection data, reconstruct the projection data into images, and further process the image data for display and analysis in accordance with the present technique. An imaging (CT) system. In the embodiment shown in FIG. 1, the imaging system 10 includes an x-ray irradiation source 12. In one exemplary embodiment, the x-ray source 12 is an x-ray tube. In other embodiments, the x-ray source 12 can generate one or more solid x-ray emitters, or indeed x-rays having a spectrum and energy useful for imaging a desired object. Any other emitter can be used.

  The radiation source 12 can be positioned proximate to the collimator 14 and the collimator can be configured to shape the radiation stream 16 irradiated by the radiation source 12. The radiation stream 16 is transmitted through an imaging volume that includes an imaging subject such as a human patient 18. The radiation stream 16 can generally be fan-shaped or cone-shaped, depending on the detector array structure discussed below and the required method of data collection. A portion of the radiation 20 passes through or around the subject and strikes the detector array, generally designated by reference numeral 22. The detector elements of the array generate an electrical signal representative of the intensity of the incident x-ray beam. These signals are collected and processed to reconstruct an image of features in the subject.

  The radiation source 12 is controlled by a system controller 24 that provides both power and control signals for the CT examination sequence. In addition, the detector 22 is coupled to a system controller 24 that commands the collection of signals generated by the detector 22. The system controller 24 can also perform various signal processing and filtering functions for initial adjustment of dynamic range, interleaving of digital image data, and the like. In general, the system controller 24 instructs the operation of the imaging system to execute an examination protocol and process the acquired data. As used herein, the system controller 24 is also typically a general-purpose or application-specific digital computer signal processing circuit, an associated storage circuit for storing programs and routines executed by the computer, and configuration parameters and image data. Includes interface circuits.

  In the embodiment shown in FIG. 1, system controller 24 is coupled to rotation subsystem 26 and linear positioning subsystem 28 via motor controller 32. In one embodiment, the rotation subsystem 26 can cause the x-ray source 12, the collimator 14, and the detector 22 to rotate around the patient 18 one or more times. In another embodiment, the rotation subsystem 26 can rotate only one of the source 12 or the detector 22, or various X-ray emitters and / or arranged in a ring around the imaging volume. The detector elements can be activated individually. In embodiments where the source 12 and / or detector 22 rotate, the rotation subsystem 26 can include a gantry. Accordingly, the system controller 24 can be used to operate the gantry. The linear positioning subsystem 28 allows the patient 18 or more specifically the patient table to be displaced linearly. Accordingly, the patient table can be moved linearly within the gantry to generate an image of a specific area of the patient 18.

  In addition, as will be appreciated by those skilled in the art, the radiation source 12 can be controlled by an x-ray controller 30 located within the system controller 24. Specifically, the X-ray controller 30 is configured to supply power and timing signals to the X-ray source 12.

  In addition, the system controller 24 is also shown to include a data collection system 34. In the exemplary embodiment, detector 22 is coupled to system controller 24, and more specifically to data collection system 34. Data collection system 34 receives the data collected by the readout electronics of detector 22. Data collection system 34 typically receives a sampled analog signal from detector 22 and converts the data to a digital signal for subsequent processing by computer 36.

  Computer 36 is typically coupled to or incorporates system controller 24. The data collected by the data collection system 34 can be transmitted to the computer 36 for subsequent processing and reconstruction. Computer 36 includes or can communicate with memory 38 that can store data processed by computer 36 or data to be processed by computer 36. It should be understood that such an exemplary system 10 can use any type of memory configured to store large amounts of data. In addition, the memory 38 includes remote components, such as memory media that may be located in the collection system or accessible to the network, and store data, processing parameters, and / or routines for performing the methods described below. can do.

  Computer 36 can also be adapted to control functions such as scanning operations and data collection that can be enabled by system controller 24. In addition, the computer 36 can be configured to receive commands and scanning parameters from an operator via an operator workstation 40, typically equipped with a keyboard and other input devices (not shown). The operator can thereby control the system 10 via the input device. Thus, the operator can observe the reconstructed image from the computer 36 and other data related to the system, begin imaging, and the like.

  A reconstructed image can be viewed using a display device 42 coupled to the operator workstation 40. In addition, the scanned image can also be printed by printer 44, which can be coupled to operator workstation 40. Display device 42 and printer 44 may also be connected to computer 36 directly or through operator workstation 40. The operator workstation 40 can also be coupled to a medical image storage management system (PACS) 46. The PACS 46 can be coupled to a remote system 48, a radiology department information system (RIS), a hospital information system (HIS), or an internal or external network that allows others at another location to access image data. Please note that it will be possible. In addition, a computer-aided diagnosis (CAD) unit 50 can be operatively coupled to the computer 36. It can be noted that the CAD unit 50 can include software configured to apply a CAD algorithm to the image data. Furthermore, the CAD unit 50 can also be coupled to a display device 42, where the image data can be displayed in the form of a display device. In practice, the CAD unit 50 may be part of the data collection system 34, or may be a completely separate component that is typically remote from the data collection system 34, such as a PACS 46, etc. The image data stored in the memory can be analyzed.

  It should further be noted that the computer 36 and operator workstation 40 can be coupled to other output devices, which can include general purpose or special purpose computer monitors and associated processing circuitry. is there. One or more operator workstations 40 can further be linked to the system for outputting system parameters, requesting examinations, viewing images, and the like. In general, display devices, printers, workstations, and similar devices supplied in the system can be on-premises connections to data collection components, or the Internet, virtual private networks, or the like May be remotely connected from these components, such as a research institution linked to the image acquisition system via one or more configurable networks, or other locations in the hospital or entirely different locations, etc. .

  As mentioned above, the exemplary imaging system used in this embodiment may be a CT scan system 52 as shown in more detail in FIG. The CT scan system 52 may be a multi-slice detector CT (MDCT) system that provides a wide array of axial coverage, high gantry rotational speed, and high spatial resolution. Alternatively, the CT scan system 52 can use volumetric CT (VCT) to image a volume, such as the entire internal organ of a subject, at high or low gantry rotational speeds using a cone beam geometry and area detector. It can be a system. The CT scan system 52 is shown with a frame 54 having an aperture 58 through which the patient 18 can be moved and a gantry 56. The patient table 60 can be positioned within the aperture 54 of the frame 54 and gantry 56 to facilitate movement of the patient 18 due to the linear displacement of the table 60, usually by the linear positioning subsystem 28 (see FIG. 1). The gantry 56 is shown with a radiation source 12 such as an X-ray tube that emits X-rays from a focal point 62. In cardiac imaging, the radiation flow is directed toward a cross section of the patient 18 that includes the heart.

  In normal operation, the X-ray source 12 projects an X-ray beam from the focal point 62 toward the detector array 22 (see FIG. 1). A collimator 14 (see FIG. 1), such as a lead or tungsten shutter, typically determines the size and shape of the X-ray beam emerging from the X-ray source 12. The detector 22 is typically formed by a plurality of detector elements that detect and transmit X-rays that pass through and around a subject of interest such as the heart or chest. Each detector element generates an electrical signal that represents the intensity of the x-ray beam at the position of the element at the time the beam strikes the detector 22. The gantry 56 rotates around the subject of interest so that multiple radiation views can be collected by the computer 36.

  Thus, as the x-ray source 12 and detector 22 rotate, the detector 22 collects data regarding the attenuated x-ray beam. The data collected from the detector 22 is then subjected to preprocessing and calibration to adjust the data to represent the line integral of the attenuation coefficient of the scanned object. The processed data, commonly referred to as projection, is then filtered and backprojected to form an image of the scanned area. The formed image can incorporate approximately 360 degrees of projection data in a particular mode.

  When reconstructed, the images generated by the system of FIGS. 1 and 2 reveal internal features 66 of the patient 18. In conventional approaches to diagnosis of a disease state, more generally a medical condition or event, a radiologist or physician will review the reconstructed image 64 to identify unique features of interest. In conventional approaches to diagnosing pathologies, and more generally medical conditions such as medical events, the radiologist or physician examines a hard copy of the image 64 display to identify unique features of interest. Become. Such features can include specific anatomical structures or organ lesions, size and shape, as well as other identifiable features of individual medical personnel skill and knowledge base images. Other analyzes can be based on the capabilities of various CAD algorithms.

  The technique performs some of these functions with a CAD algorithm. As will be appreciated by those skilled in the art, CAD algorithms can provide the potential to identify or at least locate specific features of interest such as anatomical abnormalities. The particular CAD algorithm is generally selected depending on the type of feature to be identified and the imaging diagnostic device used to generate the image data. CAD techniques can use a segmentation algorithm that knows image features or predictions such as edges, identifiable structures, boundaries, color or intensity changes or transitions, changes or transitions in spectroscopic information, etc. The feature of interest is identified by referring to the image feature to be played. Modern CAD algorithms generally provide the potential to identify these features. As a result, subsequent processing and data collection is entirely at the discretion of the healthcare professional and is based on his expertise.

  The CAD algorithm can be thought of as including several parts or modules, all of which can be implemented with this technique. In general, CAD algorithms can include modules such as access to image data, data or image segmentation, feature selection or extraction, classification, training, and visualization. In addition, CAD processing can be performed on a collection of projection data before reconstruction, 2D reconstruction data (both axial and scout mode), 3D reconstruction data (volume data or multi-plane reformatting), or It can be performed on suitable combinations. The collected projection data set can have several one-dimensional projections in a two-dimensional scan or several two-dimensional projections in a three-dimensional scan. The collected or reconstructed data can be used to perform segmentation, feature selection, and classification prior to visualization. These basic processes can be performed in parallel or in various combinations.

  The data on which the CAD algorithm is implemented may be raw image acquisition system information or may be partially or fully processed data. The data may originate from a tomographic data source, or raw data of projection or radon regions in CT imaging, single or multiple reconstructed 2D images, or 3D reconstructed volumetric image data It can be diagnostic tomography data such as.

  The divided portion of the CAD algorithm can identify a specific region of interest based on the computed features of tomographic data. The region of interest can be obtained by various methods using the whole or a part of a data set such as a lump candidate portion in a specific range. The particular partitioning method depends on the anatomical structure to be identified and is typically iterative thresholding, K-means partitioning, edge detection, edge connection, curve approximation, curve smoothing, 2D and 3D It can be based on morphological filtering, region expansion, fuzzy clustering, image / volume measurement, empirical rules, knowledge base rules, decision trees, neural networks, etc. Alternatively, the division can be at least partially manual. Automatic segmentation can also automatically draw a region of interest using prior knowledge such as the shape and size of the lump.

  A feature extraction aspect of the CD algorithm includes performing a calculation on data including a desired image. A plurality of feature criteria can be extracted from the image based data using region of interest statistics such as shape, size, density, and curvature. In projection space data, features such as the position, shape or size of the feature projection in the view or position can be used to provide consistency between views, and so on.

  The classification aspects of the CAD algorithm can again be partially or fully manually or automatically. Specifically, the region of interest can be specifically identified by using classification to classify into normal or abnormal anatomical structures or lesions. Among other things, Bayesian classifiers, neural networks, rule-based methods, or fuzzy logic methods can be used for classification. Note that more than one CAD algorithm can be used in parallel. Such parallel operations may include performing a CAD operation separately on a portion of the image data and combining the results of all the CAD operations (logically by “and” or “or” operations or both). it can. In addition, CAD operations that detect multiple conditions or anatomical features of interest can be performed sequentially or in parallel.

  Prior classification by training can be incorporated before classifying anatomical lumps using CAD algorithms. The training phase can include the calculation of several candidate features for known samples of normal and abnormal lesions or other features of interest. The feature selection algorithm can then be used to reorder candidate features to select only useful features and remove information that does not provide information or redundant information. This decision is based on the classification results using various combinations of candidate features. For practical reasons, feature selection algorithms can be used to reduce processing, storage, and data transfer dimensions. Thus, optimal discrimination between features or anatomical structures identified by the CAD algorithm can be performed.

  The visualization aspect of the CAD algorithm can reconstruct images useful for human or machine observers to perform inspections. Accordingly, various types of images can be presented to the attending physician or any other person in need of such information based on any or all of the processes and modules performed by the CAD algorithm. Visualization can include 2D or 3D rendering, marker overlay, color or intensity changes, and the like.

  FIG. 3 shows a flowchart of exemplary stages for executing a processing routine based on CAD analysis. The technique summarized in FIG. 3 begins at step 68 where image data can be collected. As described above, this data collection can be based on any suitable imaging diagnostic device that is typically selected according to the particular anatomy to be imaged and the analysis to be performed. By way of example, as will be appreciated by those skilled in the art, the physical limitations of some imaging diagnostic devices are more suitable for soft tissue imaging as opposed to bone or other dense tissue or objects. It will be something. In addition, diagnostic devices are usually tied to specific settings determined by the physical characteristics of the system, such as high or low contrast images, volume rendering, sensitive or insensitive to specific tissues or components, etc. Bring. Ultimately, image acquisition can be combined with the use of contrast agents or other markers that are used to target or enhance specific features or regions of interest. For example, in a CT system, the image data collection of step 68 is typically initiated by an operator connected to the system via operator workstation 40 (see FIG. 1). The readout electronics detect signals generated by radiation impinging on the scanner detector and the system processes these signals to produce useful image data. However, as will be appreciated by those skilled in the art, the image data can also be accessed from an image acquisition device such as, but not limited to, a magnetic resonance imaging (MRI) system or an x-ray device. In addition, image data can be collected directly from patient 18 (see FIG. 1) using the image collection device described hereinabove, and the image data can instead be collected from an archive site or data storage device. Data can be included.

  In step 70 of FIG. 3, an initial image can be formed. The formation of the image in step 70 can include image reconstruction and display or simple processing of the image data. In general, the reconstructed image can be used by a physician or system operator to guide in subsequent processing or image data collection steps. In other situations, the image is actually reconstructed and displayed. As described above, the initial image can be reconstructed and the CAD algorithm applied to the image data as described herein, but the analysis can be performed in part without such initial visualization. Note that or it can be done completely.

  In step 72, the region of interest can be identified. The region of interest may be larger or smaller than the same region of the initial image, or may be another region or an adjacent region. By way of example, the region of interest selected in step 72 can provide greater spatial resolution (eg, zoom in) for potential lesions. Furthermore, specific regions of interest can be identified based on calculated features of all or part of the collected image data set.

  Subsequently, in step 74, the feature of interest can be extracted from the image data corresponding to the identified region of interest. Interest features can be extracted by using statistics of the region of interest such as shape, size, density, and curvature.

  The feature extraction step 74 can be followed by a classification step 76. The extracted features can be classified into normal lesions and abnormal lesions in the classification step 76. In addition, a classification step can be used to specifically identify normal or abnormal anatomical structures or lesions. Among other things, Bayes classifiers, neural networks, rule-based methods, or fuzzy logic methods can be used to classify the extracted features of interest.

  The result of step 76 may be a sorted feature data set that can then be processed to be presented visually for inspection by human or machine observation. In step 78, additional images can be reconstructed for inspection by human or machine observation. The results of the classification step may be displayed separately or may be combined to be displayed as a single image. The image can then be presented to the radiologist or physician at step 80.

  Referring now to FIG. 4, a flowchart illustrating steps of computer-aided detection of an anatomical object of interest from image data according to aspects of the present technique is shown. As will be appreciated, the initial selection of possible candidates for the object of interest is an important and difficult problem in CAD methods used for anatomical shape detection. Typically, a candidate generator can be used to identify or select potential candidates for the object of interest. According to aspects of the present technique, the candidate generator can be configured to select potential candidates for a number of objects of interest and advantageously maintain a high true positive detection rate. it can. Further, anatomical shape detection can be configured to include a two-step method. The first stage can be used to select possible candidates for the object of interest from the image data. Possible candidates for the selected object of interest may include a number of false positives. The selection process can then be followed by a classification step, where a large number of false positives can be reduced by the classifier. This process of initial selection of possible candidates for the object of interest will be described in detail with reference to FIG.

  In the embodiment illustrated in FIG. 4, image data 82 collected by the imaging system can be accessed. In the illustrated method, image data 82 collected for a patient 18 (see FIG. 1) is rendered using an imaging system, such as by the CT scan system of FIG. However, as will be appreciated by those skilled in the art, the image data 82 may be collected by an image acquisition device such as, but not limited to, a magnetic resonance imaging (MRI) system or an x-ray device. In addition, image data 82 can be collected directly from the patient 18 (see FIG. 1) using the image collection device described hereinabove, or the image data 82 can be stored at an archive site or data storage device. Data from can be included.

  Subsequently, in step 84, possible candidates for a plurality of objects of interest can be selected from the image data 82. According to aspects of the present technique, possible candidates for multiple objects of interest can be selected based on a three-dimensional candidate generator. In other words, the candidate generator can be used as a filter configured to select a candidate for an object of interest based on features such as an image intensity profile, size and shape characteristics of the candidate for the object of interest. As used herein, “possible candidate for an object of interest” means a suspicious anatomical feature that can undergo subsequent diagnostic imaging, biopsy, functional testing, or other methods. For example, possible candidates for the object of interest in the lung region can include nodules. Similarly, colon polyps can represent potential candidates of interest in the colon region.

  In one embodiment, the three-dimensional candidate generator can be based on a differential operator such as a curvature tensor. Alternatively, possible candidates for the object of interest can be selected based on the structure tensor. A three-dimensional candidate generator can be used to facilitate the selection of anatomical features that can be of high interest to the radiologist or physician. As will be appreciated by those skilled in the art, additional techniques for the selection of anatomical features of interest such as the Hesse tensor have been proposed. However, the use of the Hessian tensor as a candidate generator does not yield substantially satisfactory results. Further, as discussed in more detail herein below, curvature tensors can be used to enable superior candidate selection. Moreover, candidate selection can be facilitated by using a structure tensor.

  As described above, the curvature tensor can be used to select possible candidates for the object of interest. For example, in step 84, the image data 82 can be examined to obtain potential candidates for the object of interest. Accordingly, in one embodiment, the selection of possible candidates for the object of interest is described with reference to a curvature tensor as described herein below.

As can be seen, the volume image I can be defined as an infinitely differentiable (C ^∞ ) mapping from the compact region V⊂R ³ to R, ie

（１）

(1)

It is. Here, x represents a point.

  The directionality profile of the volume image I and the direction v ≠ 0 (normally ‖v‖ = 1) at the point x is a mapping

（２）

(2)

Can be determined as In the above equation, t is a position parameter along the direction v.

Let the vector x∈V be [x ₁ x ₂ x ₃ ]. The gradient of the volume image I (x), i.e. the gradient vector, can be represented by a vector ∇I of the first derivative, i.e.

（３）

(3)

It is.

Since the region V of I is compact, the boundary of I reaches V. In other words, there exist x _min and x _max εV such that I _min = I (x _min ) ≦ I (x) ≦ I (x _max ) = I _max for all xεV. Further, for a given k such that I _min <k <I _max (where k represents the intensity level in the image of the function I),

（４）

(4)

Defines an isosurface M _k ⊂V at a point x satisfying equation (4) and ∇I (x) ≠ 0.

The isosurface M _k can be expressed as:

（５）

(5)

  Equation (5) is a parameter format like a two-dimensional manifold.

（６）

(6)

Can be rewritten. Here, M is an area of parameters u and v.

Furthermore, the isosurface M _k can be oriented and the normal vector field can be given by:

（７）

(7)

  As described above, the curvature tensor can be used to facilitate selection of potential candidates for the object of interest. The use of curvature tensors as candidate generators is described herein below.

The shape operator s _x (v) is a linear operator that maps the tangent vector v at a given point x on the surface to the direction in which the surface normal changes as x moves over the surface along the direction v. Can be defined, ie

（８）

(8)

It is.

  Thus, the shape operator of the isosurface I (x) = k can be given by

（９）

(9)

Here, H represents the Hessian of the volume image I (x), that is, the Hessian matrix.

Further, the curvature κ (x, v) of the isosurface I (x) = k in the direction v such that the point x and v ^T ∇I = 0 can be expressed as follows.

（１０）

(10)

  The main direction of the isosurface is the solution to the eigenvalue problem:

（１１）

(11)

Where ‖v‖ = 1 and v ^T ∇I = 0.

  Furthermore, the main curvature represents the associated minimum and maximum values of κ. For example, if N is a 3 × 2 matrix in a two-dimensional zero space with ∇I, the problem is the eigenvalue problem

（１２）

(12)

Can be reformulated as

As a condition.

  The main direction is then given by

（１３）

(13)

here,

Is the solution of equation (12).

Further, the corresponding principal curvatures are eigenvalues κ ₁ and κ ₂ of 2 × 2 matrix −N ^T HN / ‖∇I‖ with κ ₁ ≦ κ ₂ . Next, the curvature tensor of the volume image can be determined as follows.

（１４）

(14)

At any given point, the value of the eigenvalues κ _1,2 of the curvature tensor C provides information about the local shape of the isosurface that intersects that point. The points in the elliptical surface patch with the surface normal facing outward generate κ ₁ and κ ₂ that satisfy κ ₁ > 0 and κ ₂ > 0. In addition, for a cylindrical surface with an outward normal, κ ₁ ≈0 and κ ₂ > 0. Furthermore, the saddle points are κ ₁ <0 and κ ₂ > 0.

  As a result, using information about the local shape obtained by the curvature tensor C, a methodology is presented that facilitates the discrimination between spherical and cylindrical regions in volume images according to aspects of the present technique. In one embodiment, the spherical region may represent a nodule in a lung region CT scan, and the cylindrical region may represent a blood vessel in a lung region CT scan. Therefore,

（１５）

(15)

as well as

（１６）

(16)

It is.

According to aspects of the present technique, equation (15) can be used as the spherical shape reference b _c (x) for the curvature tensor in the isosurface intersecting point x. The spherical shape reference b _c (x) can be determined as follows.

（１７）

(17)

Similarly, equation (16) can be used as the cylindrical shape criterion c _c (x) of the curvature tensor in the isosurface intersecting the point x. The cylindrical shape criterion c _c (x) can be characterized as follows.

（１８）

(18)

  Next, Equations (17) and (18) are used for the spherical and cylindrical isosurfaces under the following conditions:

（１９）

(19)

Can be configured to identify according to the achievement of Here, b _{c, 0} and c _{c, 0} are preset threshold values.

  Also, as described above, another option for the differential operation that will be applied to the volume image to select potential candidates for the object of interest is a structure tensor. Thus, in one embodiment, the selection of possible candidates for the object of interest is described herein in connection with the structure tensor as follows.

  As will be appreciated by those skilled in the art, the structural tensor can be defined as a weighted average of the outer product of the gradient vectors in the neighborhood W⊂V around the point x.

（２０）

(20)

Here, u is a point in the integration region W, and the function w (.) Is non-negative and satisfies the following condition.

（２１）

(21)

  Considering the neighborhood W of a given point in the volume image and an arbitrary direction v, the mean square of the first derivative along v of the volume image can be defined as:

（２２）

(22)

In addition, assuming that λ ₃ ≧ λ ₂ ≧ λ ₁ represents the eigenvalues of S and v ₁ , v _2, and v ₃ represent related eigenvectors, then S is symmetric, so eigenvectors v ₁ , v ₂ and v ₃ it can be seen that are orthogonal to each other. ‖V‖ = 1 constraint is the function f (v) assumes a critical value at three points, i.e., the maximum value of equal v ₃ to lambda _3, the minimum value of equal v ₁ to lambda _1, and It is a saddle point at v ₂ such that f (v ₂ ) = λ ₂ . As a result, the structure tensor S can be expressed as follows.

（２３）

(23)

  Similar to the curvature tensor, the eigenvalues of the structure tensor can be used to distinguish between spherical and cylindrical structures in the volume image. Distance function:

（２４）

(24)

Can be configured to define spherical and cylindrical shape criteria for the structural tensor.

  With continued reference to FIG. 4, at step 84, the candidate generator can be applied to the image data 82 as described above. In other words, eigenvalue analysis of candidate generators, such as curvature tensors and structure tensors, can facilitate quantification of the spherical or cylindrical shape of a voxel of a CT volume according to its neighboring shape and intensity profile. In addition, this quantification can be followed by a threshold function of a distance function that helps in determining whether the voxel belongs to a sphere or a cylinder.

  According to aspects of the present technique, a model-based method may be used to select potential candidates for an object of interest. As will be appreciated, radiologists regularly model lung nodules as spherical objects when attempting numerical measurements such as volume and growth rate. Thus, in one embodiment, the local shapes of related three-dimensional anatomical structures such as nodules and blood vessels can be modeled as ellipsoids and toric bodies, respectively. As a result of applying the candidate generator to the CT volume, the eigenvalue response in any given voxel can facilitate the labeling of voxels belonging to either nodules or blood vessels.

With continued reference to the curvature tensor, it can be seen that the eigenvalues κ ₁ and κ ₂ are independent of any transformation I ′ (x) = ai (x) + b applied to the image volume I (x). Furthermore, as described above, the shape of the isosurface where the voxel x is located is important. It is therefore sufficient that the anatomical model of the relevant structure to be considered takes into account the shape of the isosurface of the structure.

  Referring now to detecting anatomical shapes in the lung region, lung nodules can be modeled elliptically according to aspects of the present technique. Under an appropriate curvature retention mapping, the implicit function of the ellipse ε is

（２５）

(25)

Here, a ≦ b ≦ c represents the length of the half axis of the ellipse.

ε surface point x = [xyz] at ^T

Can be defined as follows:

（２６）

(26)

here,

（２７）

(27)

It is.

It is shown that the minimum value of is given by

（２８）

(28)

  Furthermore,

Each of the minimum and maximum values can be defined as follows:

（２９）

(29)

  Similarly, a blood vessel can be modeled as a torus T having a small diameter r and a large diameter R. The torus can then be parameterized as follows:

（３０）

(30)

Here, (θ, ψ) ∈ (−π, π).

At the point x = ^{[xyz] T} on the surface T

Can be defined as follows:

（３１）

(31)

  Thus, the minimum and maximum values can be defined as follows:

（３２）

(32)

As a result, the value κ ^T _r = κ ^T ₁ / κ ^T ₂ for a torus with radii r and R is

（３３）

(33)

It is.

Furthermore, the analysis presented hereinabove can be used to set the ranges of κ ₁ and κ ₂ and the thresholds of equations (17) and (18). Therefore, in the case of an elliptic model, the range of values for a, b, and c can be set based on the target size of the significant nodule. As a result, given a, b, and c, thresholds for κ ^T _{1, min} , κ ^T _{2, min} and κ ^T _{r, min} can be set. In one embodiment, assuming that the nodule is substantially circular, the lower bound of the target nodule aspect ratio can be set to ½. Similarly, for blood vessels, typically R >> r, and thus κ ^T _{1, min} ≈0. In addition, the surface of the junction of blood vessels can be modeled as a highly bent torus, resulting in κ ^T ₁ << 0.

In the structure tensor, the intensity profile at voxel x plays an important role in the response difference at that point. Thus, the nodule has a Gaussian intensity profile with a diagonal covariance matrix Σ such that Σ _1,1 = 1 / a ² , Σ _2,2 = 1 / b ² , and Σ _3,3 = 1 / c ^2. It can be modeled by using it.

  According to yet another aspect of the present technique, the Hesse tensor can be used as a three-dimensional candidate generator. As can be seen, the Hessian or Hessian of the volume image I (x) is the matrix H of the second derivative, i.e.

（３４）

(34)

It is.

The eigenvalue of Hessian H can also be used to give information about the direction profile of the volume image I (x) in an arbitrary direction v. For example, if all eigenvalues of H are large negative, the volume direction profile is strongly curved. In addition, if two of the eigenvalues are large negative and the third eigenvalue has a small magnitude, there are two critical directions in which the direction profile is strongly curved and one critical direction in which the direction profile is somewhat flat. There is a direction. Further, assuming that the _three eigenvalues of H are λ ₃ ≦ λ ₂ ≦ λ ₁ , the spherical shape criterion of the Hesse tensor can be defined as follows:

（３５）

(35)

  Similarly, the cylindrical shape criterion of the Hesse tensor can be characterized as follows:

（３６）

(36)

  Next, like the curvature tensor, the eigenvalues of the Hesse tensor can be used to distinguish between spherical and cylindrical structures in the volume image. Distance function

（３７）

(37)

Can be configured to define the spherical and cylindrical shape criteria of the Hesse tensor.

  In other words, using equations (35) and (36) at a given point in the volume image,

（３８）

(38)

Can be roughly classified as belonging to a spherical or cylindrical region. Here, b _{H, 0} and c _{H, 0} are preset threshold values.

  However, as mentioned above, the Hesse tensor does not fully satisfy the identification of possible candidates for the object of interest and is therefore not suitable as a candidate generator. Specifically, the Hesse tensor is strongly dependent on the shape of the intensity profile.

  With continued reference to step 84, possible candidates for the object of interest can be selected by applying a three-dimensional candidate generator to the image data 82. The effect of noise can be reduced by smoothing the image data showing the volume of interest. Subsequently, as described above, possible candidates for the object of interest can be selected by performing eigenvalue analysis of the candidate generator. In other words, the eigenvalues of the 3D candidate generator can be calculated for each voxel of the volume of interest. Further, the threshold can be derived from the anatomical model as described above. The eigenvalues and thresholds can then be used to facilitate the identification of possible candidate shapes of interest. As a result, each voxel of the volume of interest can be labeled as spherical or cylindrical. Alternatively, voxels may not be labeled. As a result of step 84, at this point, individual voxels are selected as possible candidates 86 of interest for presentation to the radiologist.

  Following step 86, possible candidates 86 of the selected object of interest may be classified at step 88. As will be appreciated, the potential candidate 86 for the selected object of interest may include a number of false positives. The classification step can facilitate the categorization of possible selected objects of interest 86 into normal and abnormal objects. Classification of potential candidates 86 of interest may include one of an artificial neural network, Bayesian analyzer, linear discriminant analyzer, or K nearest neighbor classifier as potential candidate 86 of interest. Can include applying. As mentioned above, the possible candidates 86 of the selected object of interest may contain a substantial number of false positives. Classifying potential candidates 86 of multiple objects of interest advantageously results in a set of objects of interest with a low false positive rate, thereby facilitating easier radiologist examination Can do. As described above, the set of classified objects can then be subjected to a visualization step that facilitates reconstruction of the image for presentation to a radiologist or physician. The set of classified objects can be displayed individually. Alternatively, the classified set of objects may be highlighted and superimposed on the initial image and displayed as a single image.

  The techniques for detecting anatomical shapes described hereinabove can be described in detail with reference to FIGS. Referring now to FIG. 5, an image 90 of a pulmonary pleural region of a subject 18 of interest collected by an imaging system of the type shown in FIG. 1 is schematically shown. Reference numeral 92 represents the left lung, and reference numeral 94 corresponds to the right lung. In addition, the pulmonary pleural region is indicated by reference numeral 96. A plurality of possible candidates 98 of interest can be selected from the image data by applying a candidate generator as described above. In one embodiment, a curvature tensor can be used as a candidate generator. Alternatively, structural tensors may be used as candidate generators.

  As will be appreciated by those skilled in the art, the segmented pulmonary pleural region 96 may include nodules, blood vessels, tracheal walls, and ribs. Isolated pulmonary nodules (SPNs) are small circular or ovoid lesions (abnormal tissues) in the lung. SPNs are usually asymptomatic and are generally found by chance on chest x-rays performed for other reasons. They are usually smaller than 3 cm to 4 cm in diameter (not larger than 6 cm) and are always surrounded by normally functioning lung tissue.

  It may be desirable to select nodules from the image data. However, many nodules may have the same CT value as blood vessels and tracheal walls. As a result, many blood vessels and tracheal walls may be included as nodule candidates, which contributes to many false positives. Thus, the sensitivity of the candidate generator now includes a pool of all possible candidates for the object of interest that are substantially spherical, and can reliably avoid substantially cylindrical regions such as blood vessels. . Furthermore, a set of objects with a reduced false positive rate can be generated by classifying a pool of candidate objects of interest and identifying normal and abnormal candidate objects of interest.

  As described above, potential candidates 98 of multiple objects of interest can be selected from the image data representing the volume of interest by eigenvalue analysis of each voxel of the volume of interest. For example, potential candidates for a plurality of substantially spherical objects of interest can be selected based on the eigenvalues of the candidate generator. A plurality of possible candidates for a substantially spherical object of interest may represent a pulmonary nodule of the pulmonary pleural region 96. In addition, possible candidates for a plurality of substantially cylindrical objects of interest can be selected based on the eigenvalues of the candidate generator. Possible candidates for a plurality of substantially cylindrical objects of interest can include, for example, blood vessels in the pulmonary pleural region 96. As a result, possible candidates for a plurality of substantially spherical objects of interest representing pulmonary nodules are derived from the image data by selectively distinguishing between substantially spherical and substantially cylindrical structures. You can choose.

  As will be appreciated, the potential candidates 98 of the selected plurality of objects of interest may include a high false positive rate. Thus, it may be desirable to generate a reduced set of potential candidates for the object of interest. As a result, possible candidates 98 of interest can be classified to generate a set 100 of interest with a reduced false positive rate. The set of objects 100 can then be highlighted and superimposed on the image and presented to the radiologist. As described above, one of an artificial neural network, Bayesian analyzer, linear discriminant analyzer, or K nearest neighbor classifier can be used as a classification tool.

  Following the classification step, an image based on the plurality of classified candidate objects of interest can be generated. FIG. 6 is a schematic diagram of medical image data 102 including possible candidates 98 of interest and classified candidate objects 100 of interest. In the illustrated embodiment, the classified candidate object of interest 100 can be highlighted and superimposed on the image 102 and presented to the radiologist. Alternatively, the classified target object candidates 100 may be displayed separately.

  Turning now to the selection of possible candidates for an object of interest in the colon region, as will be appreciated by those skilled in the art, the colon region can include polyps and normal bowel fistulas. It may be desirable to select a polyp from the image data. However, many polyps may have the same CT value as normal bowel fistulas in the colon. As a result, many intestinal fistulas may be included as polyp candidates, thereby contributing to a number of false positives. Thus, the sensitivity of the candidate generator will include a pool of all possible candidates for the object of interest that are substantially cylindrical, while ensuring that common bowel fistulas in the colon are avoided. Alternatively, in one embodiment, the sensitivity of the candidate generator may include a pool of all possible candidates for the object of interest that are substantially spherical. Furthermore, a pool of candidate objects of interest can be classified to generate a set of objects with reduced false positives by identifying normal and abnormal candidate objects of interest.

  FIG. 7 is a schematic diagram of an image 104 of the colon 106 of a subject of interest 18 (see FIG. 1) collected by an imaging system of the type shown in FIG. 1 according to aspects of the present technique. Reference numeral 108 represents an area of the colon 106. Further, a plurality of polyps 110 representing possible candidates for the object of interest in the region 108 of the colon 106 can be selected based on the eigenvalues of the candidate generator, as described above. As will be appreciated, polyps are tumors that develop in the colon and other organs. These tumors, or sarcomas, are shaped like mushrooms or dome-like buttons and occur inside the lining of the colon. These can be as small as a granule or larger than a plum. Colon polyps begin with benign tumors and may eventually become malignant. The larger the polyp, the more likely it is to contain cancer cells. Furthermore, based on the sensitivity of the candidate generator, multiple natural selections (not shown) of the colon 106 can also be selected as possible candidates for the object of interest. The polyp 110 can then be selected from the image data by selectively distinguishing between the substantially cylindrical polyp 110 and the colonic fold 106. Alternatively, as described above, the polyp 110 can be selected from the image data by selectively identifying the substantially spherical polyp 110 and the colonic fold 106.

  Turning now to FIG. 8, an enlarged view of an image of region 108 of colon 106 is shown. The enlarged view of FIG. 8 shows possible selected objects of interest, such as a polyp 112 on the stem and a polyp 114 that spreads flat. Again, as described above, possible candidates for the selected object of interest may include a high false positive rate. A reduced set 118 of potential candidates for the object of interest can be generated by classifying the potential candidates 110 of the plurality of selected objects of interest. FIG. 9 is a schematic view of medical image data 116 including a polyp 112 on the stem and a polyp 114 spreading flat. The classified candidate object of interest 118 is then highlighted in the image 116 and can be presented to the radiologist.

  The method of detecting an anatomical shape described herein can advantageously efficiently select possible candidates for an object of interest that can be present in the image data 82. The efficient selection of potential candidates of interest is followed by a classification step to help generate a set of objects with a reduced false positive rate so that the radiologist or doctor can interpret the images. It makes it easier to detect suspicious areas that may indicate illness.

  While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. Accordingly, the appended claims should be understood to cover all such modifications and changes as fall within the true spirit of this invention. Further, the reference numerals in the claims corresponding to the reference numerals in the drawings are merely used for easier understanding of the present invention, and are not intended to narrow the scope of the present invention. Absent. The matters described in the claims of the present application are incorporated into the specification and become a part of the description items of the specification.

1 is a schematic diagram of an exemplary imaging system in the form of a CT imaging system for use in generating an image processed in accordance with aspects of the present invention and analyzing the image and its underlying image data. 2 is a schematic diagram of a physical implementation of the CT system of FIG. 6 is a flowchart illustrating exemplary steps in logic for performing image data processing based on CAD analysis of collected image data in accordance with aspects of the present technique. 6 is a flowchart illustrating exemplary steps for detection of possible candidates for an object of interest in accordance with aspects of the present technique. FIG. 2 is a schematic illustration of an image of a lung pleural region of a subject of interest collected and analyzed by an imaging system of the type shown in FIG. 1 in accordance with aspects of the present technique. 1 is a schematic diagram of medical image data including features that will be detected and classified according to aspects of the present technique. FIG. FIG. 2 is a schematic diagram of an image of a colon region of a subject of interest collected and analyzed by an imaging system of the type shown in FIG. 1 in accordance with aspects of the present technique. FIG. 8 is an enlarged view of an image of the colon region of the subject of interest of FIG. 7 analyzed according to aspects of the present technique. FIG. 3 is a schematic diagram of medical image data including features detected and classified according to aspects of the present technique.

Explanation of symbols

30 X-ray controller 32 Motor controller 34 Data acquisition system 36 Computer 38 Memory 40 Operator workstation 42 Display device 44 Printer 48 Remote client 50 CAD unit

Claims (10)

A computer-aided method for detecting anatomical objects of interest from image data,
Selecting possible candidates (86) of a plurality of objects of interest from the image data (82) based on a three-dimensional candidate generator including a curvature tensor;
Classifying the potential candidates (86) of the plurality of objects of interest to generate a set of objects of interest with a reduced false positive rate;
Including methods.   Accessing the image data (82), wherein the image data (82) is acquired by an image acquisition device including one of a computed tomography system, a magnetic resonance imaging system, and an x-ray device. The method of claim 1.   The method of claim 1, further comprising generating an image based on possible candidates for the plurality of classified objects of interest.   Selecting the potential candidates (86) of the plurality of objects of interest facilitates eigenvalue analysis of the curvature tensor to facilitate identification of the shape of the potential candidates (86) of the objects of interest. The method of claim 1 comprising the step of:   Classifying the potential candidates (86) of the plurality of objects of interest comprises selecting one of an artificial neural network, Bayesian analyzer, linear discriminant analyzer, or K nearest neighbor classifier as the object of interest. The method of claim 1 including applying to a possible candidate (86). A computer readable medium comprising one or more tangible media, the one or more tangible representation media comprising:
Code adapted to select potential candidates (86) of a plurality of objects of interest from image data (82) based on a three-dimensional candidate generator that includes a curvature tensor;
Code adapted to classify the potential candidates (86) of the plurality of objects of interest to generate a set of objects of interest with a reduced false positive rate;
A computer readable medium including: A system for computer-aided detection of anatomical objects of interest from image data,
A computer system (36) operably coupled to one of a CT image acquisition system or a CT image storage system;
The computer system (36) includes a three-dimensional candidate generator, the three-dimensional candidate generator configured to select a plurality of potential candidates (86) of interest from the image data (82). And the three-dimensional candidate generator includes a curvature tensor. A CT imaging system (10) comprising:
An X-ray source (12) configured to irradiate a stream of radiation (16);
An area detector (22) comprising a plurality of detector elements configured to detect the radiation stream (16) and to generate one or more signals responsive to the radiation stream (16); ,
One or more sets of projection data from one or more of the detector elements via a data acquisition system (34) rotating the x-ray source (12) and the area detector (22) A system controller (24) configured to collect
A computer system (36) operably coupled to one of a CT image acquisition system or a CT image storage system;
With
The computer system (36) includes a three-dimensional candidate generator, the three-dimensional candidate generator configured to select a plurality of potential candidates (86) of interest from the image data (82). And the three-dimensional candidate generator includes a curvature tensor.   The classifier of claim 8, further comprising a classifier configured to classify the potential candidates (86) of the plurality of objects of interest to generate a set of objects of interest with a reduced false positive rate. CT imaging system (10).   An operator workstation (40) operably coupled to the computer system (36) is further configured, wherein the operator workstation (40) is configured to display the sorted set of objects of interest. A CT imaging system (10) according to claim 8.

Images