JP6578375B2 - Image processing apparatus and image processing method - Google Patents

Description

  The present invention relates to an image processing apparatus and an image processing method, and more particularly to extraction of a region from a medical image and evaluation thereof.

  In recent years, a computer diagnosis support apparatus (CAD; Computed Aided Diagnosis) that extracts abnormal shadow candidates such as lesions from medical images has been proposed. In CAD, shadow features are determined and extracted from various types of medical images such as X-ray images, mammography images, CT images, and MRI.

  For example, Patent Document 1 describes a process of extracting an abnormal shadow candidate by subtracting two images having different shooting dates and times. In the method of Patent Document 1, an emphasized image is created for each local area by subtracting a reference value obtained from the density value in a small area set in one image from the density value of the corresponding small area in the other image. The technology is described. This makes it possible to extract an abnormal shadow more accurately even if a positional shift occurs when the two images are differentiated.

International Publication No. 2005/009242

  However, the conventional method is mainly a method for extracting without overlooking an abnormal shadow, and there has been no consideration for evaluating the accuracy and error of the extraction result. Also, it was not considered to evaluate the effect of motion artifacts on abnormal shadow candidates.

  The present invention has been made in view of the above problems, and in the process of extracting an abnormal shadow candidate from a medical image, it is possible to obtain and output information for evaluating a motion artifact included in the extraction result. An object of the present invention is to provide an image processing apparatus and an image processing method.

  In order to achieve the above-described object, the present invention provides an input unit that inputs image data, an extraction unit that extracts an abnormal shadow candidate region from the input image data, and estimates motion artifacts existing in the abnormal shadow candidate region. An artifact estimation unit, an evaluation information calculation unit that calculates evaluation information about the possibility that the motion artifact is included in the abnormal shadow candidate region, and an output unit that outputs the evaluation information. An image processing apparatus.

  An image processing method in the image processing apparatus is a method for inputting image data, a step for extracting an abnormal shadow candidate region from the input image data, and a motion artifact existing in the abnormal shadow candidate region. An image processing method comprising: a step; calculating evaluation information about a possibility that the motion artifact is included in the abnormal shadow candidate region; and outputting the evaluation information.

  According to the present invention, it is possible to provide an image processing apparatus and an image processing method capable of obtaining and outputting information for evaluating a motion artifact included in an extraction result in a process of extracting an abnormal shadow candidate region from a medical image.

The figure which shows the whole structure of the image processing system 1 Flowchart explaining the flow of evaluation information calculation processing Flowchart for explaining the procedure of the artifact estimation process in step S103 of FIG. (a) Abnormal shadow candidate area B extracted from CT image 3, (b) Sampling points P1 to P9 set in the peripheral portion around abnormal shadow candidate area B Flowchart explaining the details of the procedure of step S202 in FIG. The figure explaining the production | generation of the theoretical image 62 of step S301 The figure explaining the profile of step S302. (a) CT image 3 (b) Theoretical image 6 The figure explaining the deconvolution of step S303 Examples of how to blur the image for each filter A diagram for explaining a line segment Qm connecting the abnormal shadow candidate region B and each sampling point Pm (m = 1 to n) A diagram for explaining the artifact component Vm at each sampling point Pm (m = 1 to n) Example of coefficient k representing the characteristics (rigidity) of tissue on line segment Qm connecting abnormal shadow candidate region B and each sampling point Pm (a) For bone, (b) For soft tissue Flowchart explaining the evaluation information calculation step of step S104 of FIG. Diagram explaining evaluation of sampling point Pm distribution The figure explaining the standard deviation SD in an abnormal shadow candidate area | region. SDi: Theoretical value, SDp: Before filtering, SDa: After filtering User interface 8 initial screen example Example of a display screen at the stage where two sampling points are arranged in the user interface 8 Example of display screen at the stage where 6 sampling points are arranged in the user interface 8

  An image processing apparatus according to the present invention includes an input unit that inputs image data, an extraction unit that extracts an abnormal shadow candidate region from the input image data, and an artifact estimation that estimates a motion artifact existing in the abnormal shadow candidate region An evaluation information calculation unit that calculates evaluation information about the possibility that the motion artifact is included in the abnormal shadow candidate region, and an output unit that outputs the evaluation information.

  Further, the artifact estimation unit obtains an artifact component at a plurality of points set in a contour portion around the abnormal shadow candidate region, and estimates the motion artifact of the abnormal shadow candidate region by integrating the artifact components of each point It is characterized by doing.

  The artifact estimation unit generates a theoretical image, obtains a profile of the contour for each of the input image data and the theoretical image, and estimates the artifact component based on the degree of coincidence of the profiles. Features.

  In addition, the artifact estimation unit may determine the motion of the abnormal shadow candidate region based on the distance between the point set around the abnormal shadow candidate region and the abnormal shadow candidate, and the tissue structure around the abnormal shadow candidate region. It is characterized by estimating artifacts.

  Further, the evaluation information includes the position information of the peripheral portion around the abnormal shadow candidate region, or a first evaluation term that evaluates the size of the motion artifact, and the abnormal shadow candidate using the estimated motion artifact. And a second evaluation term based on image quality when the area is temporarily corrected.

  Further, the position information includes at least one evaluation item among the validity, number, and distribution of positions for a plurality of points set in a contour portion around the abnormal shadow candidate region when calculating the motion artifact. It is characterized by including.

  The image quality is evaluated by image sharpness and standard deviation.

  And a user interface for receiving input of a plurality of points set when estimating a motion artifact existing in the abnormal shadow candidate region and displaying the evaluation information calculated by the evaluation information calculation unit. To do.

  The image processing method according to the present invention includes a step of inputting image data, a step of extracting an abnormal shadow candidate region from the input image data, and a step of estimating a motion artifact existing in the abnormal shadow candidate region. And calculating the evaluation information about the possibility that the motion artifact is included in the abnormal shadow candidate region, and outputting the evaluation information.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

  First, the configuration of an image processing system 1 to which the image processing apparatus 100 of the present invention is applied will be described with reference to FIG.

  As shown in FIG. 1, the image processing system 1 includes an image processing device 100 having a display device 107 and an input device 109, an image database 111 connected to the image processing device 100 via a network 110, and a medical image photographing device. 112.

  The image processing apparatus 100 is a computer that performs processing such as image generation and image analysis. As shown in FIG. 1, the image processing apparatus 100 includes a CPU (Central Processing Unit) 101, a main memory 102, a storage device 103, a communication interface (communication I / F) 104, a display memory 105, a mouse 108, and other external devices. Interface (I / F) 106, and each unit is connected via a bus 113.

  The CPU 101 calls and executes a program stored in the main memory 102 or the storage device 103 in the work memory area on the RAM of the main memory 102, executes driving control of each unit connected via the bus 113, and the image processing apparatus Implements various processes performed by 100.

  The CPU 101 executes an evaluation information calculation process (see FIG. 2) for generating information for evaluating the accuracy of the abnormal shadow candidate extracted from the medical image. In the evaluation information calculation process, in particular, evaluation information for evaluating how much the abnormal shadow candidate area extracted from the image is affected by the artifact (possibility that the abnormal shadow candidate area includes a motion artifact) is obtained. Details of the evaluation information calculation process will be described later.

  The main memory 102 includes a ROM (Read Only Memory), a RAM (Random Access Memory), and the like. The ROM permanently stores programs such as computer boot programs and BIOS, and data. The RAM temporarily holds programs, data, and the like loaded from the ROM, the storage device 103, and the like, and includes a work area that the CPU 101 uses for performing various processes.

  The storage device 103 is a storage device that reads / writes data to / from an HDD (hard disk drive) or other recording medium, and stores programs executed by the CPU 101, data necessary for program execution, an OS (operating system), and the like. . As for the program, a control program corresponding to the OS and an application program are stored. Each of these program codes is read by the CPU 101 as necessary, transferred to the RAM of the main memory 102, and executed as various means.

  The communication I / F 104 includes a communication control device, a communication port, and the like, and mediates communication between the image processing apparatus 100 and the network 110. The communication I / F 104 performs communication control with the image database 111, another computer, or a medical image photographing apparatus 112 such as an X-ray CT apparatus or an MRI apparatus via the network 110.

  The I / F 106 is a port for connecting a peripheral device, and transmits / receives data to / from the peripheral device. For example, a pointing device such as a mouse 108 or a stylus pen may be connected via the I / F 106.

  The display memory 105 is a buffer that temporarily accumulates display data input from the CPU 101. The accumulated display data is output to the display device 107 at a predetermined timing.

  The display device 107 includes a display device such as a liquid crystal panel and a CRT monitor, and a logic circuit for executing display processing in cooperation with the display device, and is connected to the CPU 101 via the display memory 105. The display device 107 displays the display data stored in the display memory 105 under the control of the CPU 101.

  The input device 109 is an input device such as a keyboard, for example, and outputs various instructions and information input by the operator to the CPU 101. The operator interactively operates the image processing apparatus 100 using external devices such as the display device 107, the input device 109, and the mouse 108.

  The network 110 includes various communication networks such as a LAN (Local Area Network), a WAN (Wide Area Network), and the Internet, and mediates communication connection between the image database 111, the server, other information devices, and the image processing apparatus 100. .

  The image database 111 stores and stores image data captured by the medical image capturing device 112. In the image processing system 1 shown in FIG. 1, the image database 111 is configured to be connected to the image processing apparatus 100 via the network 110. However, for example, the image database 111 is provided in the storage device 103 in the image processing apparatus 100. May be.

  Next, the evaluation information calculation process executed by the image processing apparatus 100 will be described with reference to the flowchart of FIG.

  The CPU 101 reads image data to be processed from the image database 111 connected via the storage device 103 or the communication I / F 104 and stores it in the main memory 102 (step S101). The image data is a plurality of tomographic images obtained by imaging the subject using an X-ray CT apparatus, an MRI apparatus, or the like. Hereinafter, a case where the input image is a CT image will be described as an example.

  The CPU 101 extracts an abnormal shadow candidate B from the input image (step S102; see FIG. 4). The abnormal shadow candidate region B is a region determined as a lesion candidate, tumor candidate, nodule candidate, or the like based on the feature of the density value. A known method may be used to extract abnormal shadow candidates. As a known technique, for example, as described in Patent Document 1, there is a technique of extracting using the difference between images of the same subject and the same part with different imaging dates and times.

  Next, the CPU 101 estimates motion artifacts existing in the abnormal shadow candidate region B extracted in step S102 from the surrounding artifact components (step S103). Details of step S103 will be described later.

  Thereafter, the CPU 101 calculates evaluation information about the possibility that the motion artifact estimated in step S103 is included in the abnormal shadow candidate region B (step S104). Details of step S104 will be described later. The CPU 101 outputs the evaluation information calculated in step S104 (step S105).

  Next, the artifact estimation process in step S103 will be described with reference to FIGS.

  As shown in the flowchart of FIG. 3, the CPU 101 arranges a plurality of sampling points P for the slice or volume including the abnormal shadow candidate region B (step S201). Assume that a region B determined as a nodule is extracted from the CT image 3 as shown in FIG. Hereinafter, this region B is referred to as an abnormal shadow candidate region B. The CPU 101 arranges n sampling points P1 to Pn around the abnormal shadow candidate region B. There are no particular conditions for the location and number of sampling points, but it is preferable that the sampling points be placed at points where errors are less likely to occur in the evaluation information calculation process described later.

  Specifically, points that are not easily affected by the partial volume, points on the outline (near the gradient (edge) of the density value), points where large motion (motion artifact) is expected, points close to the abnormal shadow candidate region B, etc. It is preferable to arrange a large number of sampling points at locations where suitable conditions are met. In step S201, as shown in FIG. 4 (b), n sampling points P1 to Pn are arranged in the contour portion around the abnormal shadow candidate region B of the CT image 3. The mth sampling point is called Pm.

  Next, the CPU 101 obtains the strength and direction of the artifact component for each sampling point Pm (step S202). In order to obtain the strength and direction of the artifact component, the difference between the image sharpness and the theoretical value at each point Pm is calculated. Here, when the point Pm has an image blur due to motion artifact, the sharpness of the actual image is lower than the theoretical value (an ideal image mathematically obtained).

  The processing for obtaining the strength and direction of the artifact component in step S202 will be described with reference to FIG.

  First, the CPU 101 obtains a theoretical image 62 that is a theoretical value of an image (step S301). The calculation procedure of the theoretical image 62 is shown in FIG. The CPU 101 virtually X-rays (forward projection) the object 6 which is not affected by the partial volume and has a clear outline by simulation. When image reconstruction (backprojection) is performed from the forward projection data 61 using the same method as the method for creating the CT image 3, the characteristics of the reconstruction filter used for backprojection and the reconstruction method are used to determine the original object 6 and In comparison, an image 62 having a sharpness deteriorated is created. The image 62 created in this way is an ideal image (theoretical image 62) having no motion artifact. It should be noted that the object 6 used in the simulation can obtain highly accurate evaluation information by using an object (subject) having a sampling point P and having a material characteristic close to that of the object.

  Next, the CPU 101 takes a profile so as to straddle the contour portion for each sampling point Pm of the CT image 3 (step S302). Specifically, as shown in FIG. 7 (a), a tangent line 71 is drawn with respect to the contour portion around the abnormal shadow candidate region B of the CT image 3, and the tangent line 71 is in the range of 0 to 180 degrees. Draw a line segment 72 across the angle. Similarly, a tangent line 71p parallel to the tangent line 71 drawn in FIG. 7 (a) is drawn on the theoretical image 62 obtained in step S301 and straddles the contour at the same angle as the line segment 72 in FIG. 7 (a). Draw a line segment 72p. Then, a pixel value profile C on the line segment 72 and a pixel value profile D on the line segment 72p are obtained. As described above, the profiles C and D of the pixel values on the line segments 72 and 72p are obtained for the CT image 3 and the theoretical image 62, respectively (step S302).

  Next, the CPU 101 regards the profiles C and D generated in step S302 as a one-dimensional signal and performs deconvolution. Thereby, the difference between the image sharpness of the contour portion of the CT image 3 and the theoretical value is obtained. That is, the strength of the artifact component at the point Pm is obtained (step S303, step S202).

As shown in FIG. 8, the profile C of the contour portion of the CT image 3 and the profile D of the corresponding contour portion of the theoretical image 62 are regarded as one-dimensional signals, and deconvolution is performed. Then, a one-dimensional signal E for the profile D is obtained. This shows a characteristic equivalent to a low-pass filter of a one-dimensional signal and shows how much the profile D has deteriorated. That is, the signal C is obtained by filtering the signal D with the signal E. This process is expressed as follows.

Here, C _F , D _F , and E _F represent the Fourier transform of C, D, and E, respectively. Further, division in Fourier space can be expressed as deconvolution in real space.

  As described above, it is possible to determine how the CT image 3 has deteriorated from the theoretical image 62 at the sampling point Pm from the shape of the signal E (hereinafter referred to as filter E). Further, it can be recognized that the artifact components are included in the directions of the profiles C and D (directions of the line segments 72 and 72p). The CPU 101 calculates such profiles C and D from a plurality of directions for each sampling point Pm. This makes it possible to determine in which direction and how much the artifact component is included.

  For example, by changing the angle formed by the line segment straddling the contour part and the tangent line of the contour part, the filter E is obtained by the above-described procedure, and the filter component data for each angle is integrated to integrate the artifact component at the sampling point Pm. (Strength, direction) is obtained (Step S304, Step S305).

  FIG. 9 shows how the image is blurred for various filters E. FIG.

  Since the shape of the filter E changes depending on how the theoretical image 62 is blurred, the shape of the filter E may be converted into the strength of the artifact. In the present invention, the width of the filter E is regarded as the amount of movement of the object, and is treated as the strength of the motion artifact itself. For example, if the width of the filter E is 7 pixels, it is assumed that motion artifacts exist over 7 pixels.

  The CPU 101 performs the processing from step S301 to step S305 for all sampling points Pm. Thereby, the strength and direction of the artifact at each sampling point Pm are obtained (step S202 in FIG. 3).

Next, the CPU 101 estimates a motion artifact in the abnormal shadow candidate area B (step S203). In step S203, the CPU 101
(A) Distance between abnormal shadow candidate region B and sampling points Pm arranged around it
(B) It is desirable to consider both material characteristics between the abnormal shadow candidate region B and each sampling point Pm.

  Specifically, the CPU 101 calculates a line segment Qm connecting the point BX and the point PmX, where BX is the coordinate of a representative point in the abnormal shadow candidate region B and PmX is the coordinate of the sampling point Pm.

  This is performed for all sampling points P1 to Pn. As shown in FIG. 10 (a), line segments Q1 to Qn connecting the abnormal shadow candidate coordinates BX and each sampling point PmX are calculated.

  Through the processing in step S202, the strength and direction of the artifact are calculated for each sampling point Pm. As a result, the artifact component at each sampling point Pm can be represented by a vector Vm as shown in FIG. The motion artifacts included in the abnormal shadow candidate region B include the vectors V1 to Vn indicating the artifact components at the surrounding sampling points P1 to Pn, the distance D of the above-described (A) line segments Q1 to Qn, and (B) line It can be estimated using the material properties on the minutes Q1 to Qn. Hereinafter, a method for estimating the motion artifact included in the abnormal shadow candidate region B will be described.

The closer the sampling point Pm having an artifact component is to the abnormal shadow candidate region B, the higher the possibility that the abnormal shadow candidate region B includes motion artifacts. Therefore, the motion artifact F presumed to be included in the abnormal shadow candidate region B is expressed by the following equation (2).

  Here, Wn is expressed using the distance D of the line segment Qm and the coefficient k indicating the material property of the tissue, as shown in the equation (3). The material property of the tissue is, for example, rigid body. For example, the rigid property of the tissue is defined as the meaning of whether the tissue constituting the line segment Qm connecting two points has the property of a rigid body. The coefficient k indicates a value closer to “1” as the tissue is closer to a rigid body, and a value closer to “0” as the soft body tissue.

  As shown in FIG. 12 (a), when the object on the line segment Qm connecting the two points has a high CT value such as a bone, the coefficient k is set to a large value (k >> 0). On the other hand, as shown in FIG. 12 (b), when the object on the line segment Qm connecting the two points is a tissue with a low CT value such as a lung tissue, the coefficient k is set to a small value (k = about 0). To do. If there is more than one tissue on the line segment Qm, if there are multiple tissues, the weight corresponding to the number of pixels in each tissue on the line segment Qm is multiplied by each coefficient k, and the sum is added to the line segment Qm. The coefficient k indicating the material property of

  Note that the term δ in the above equation (2) is an error component. This is an error that is allowed from uncertain factors and empirical factors such as the number of sampling points P, the likelihood of the position, and whether it is affected by noise.

  From the above, the motion artifact F of the abnormal shadow candidate region B can be estimated.

  The image processing apparatus 100 of the present invention further obtains evaluation information for evaluating whether the motion artifact F obtained in the above-described procedure really exists in the abnormal shadow candidate region B (the evaluation in step S104 in FIG. 2). Information calculation process). The evaluation information calculation process will be described with reference to FIGS.

  In the present invention, the possibility that a motion artifact F is included in the abnormal shadow candidate region B is evaluated from the following two viewpoints.

(First evaluation term p ₁ ) The validity, number, distribution, and size of motion artifact F of the sampling points Pm set when calculating the artifact components around the abnormal shadow candidate region B Among them, at least one evaluation item is evaluated (step S401 to step S402 in FIG. 13).

(Second evaluation term p ₂ ) The image quality when the abnormal shadow candidate region B is provisionally corrected using the estimated motion artifact F is evaluated (steps S403 to S405).

First, the first evaluation term p ₁ will be described.

In the first evaluating terms p _1, evaluated for each evaluation item of the following, it is determined that evaluation as motion artifacts F is high are properly included. As described above, the evaluation item is at least one of (a) the validity of the position of the sampling point Pm, (b) the number, (c) distribution, and (d) the size of the motion artifact F.

(a) Validity of the position The evaluation value increases as the sampling point Pm is placed in a part that is less affected by the partial volume, or is difficult to understand the influence, near the contour, or where a large movement is expected. Like that. For example, it is ideal that the sampling point Pm is set at a branch portion of a blood vessel, a sternum, or the like. The evaluation value needs to be defined in advance from an anatomical viewpoint. For simplicity, in this embodiment, a flat soft tissue is evaluated as “0”, and a bone or a complex-shaped tissue is evaluated as “1”.

(b) Number of pieces It is easy to set so that the evaluation value increases as the number of sampling points Pm increases. Considering the possibility that the effect gradually fades as the number increases, the evaluation formula is the following formula (4).

  Here, n is the number of sampling points Pm and is an integer of 1 or more. The maximum evaluation value is 1 when n is infinite.

(c) Distribution This is an evaluation item for confirming how far the sampling points Pm are scattered with the coordinate BX of the abnormal shadow candidate region B as the origin. The evaluation value is set to be higher as the sampling points Pm are evenly scattered. For example, as shown in FIG. 14, the line connecting the coordinate BX of the abnormal shadow candidate region B and the sampling point Pm is represented by a vector Rm, and the sum of the vectors R1 to Rn is taken. And The evaluation value G regarding the distribution is expressed by the following equation (5).

(d) Motion Artifact F It is considered that the larger the calculated motion artifact F, the higher the possibility that the abnormal shadow candidate region B includes an artifact. For this reason, the evaluation value is set higher as the scalar amount of the vector F is larger. For example, when the maximum movement amount in the human body is Mmax, the evaluation formula can be expressed as the following formula (6).

  If the maximum movement amount in the human body is the movement amount of the chest due to respiration, Mmax = 20 mm may be set.

The sum of the evaluation values for (a) to (d) is defined as a _first evaluation term p ₁ . The maximum value is 1 (relevance) +1 (number) +1 (distribution) +1 (motion artifact F) = 4, which is set to p _1max (step S402).

Next, the second evaluation term p ₂ will be described.

In the second evaluating terms p _2, CPU 101 uses the motion artifact F calculated in step S203, temporarily corrects the abnormal shadow candidate region B (filtering) (step S403). Then, calculate how close the image quality (sharpness and standard deviation) of the image after temporary correction approaches the theoretical value, and evaluate the possibility that motion artifact F is included in the abnormal shadow candidate area B with the degree of approach (Step S404).

  As described above, the motion artifact F has a characteristic as a signal filter.

  If the calculated motion artifact F accurately indicates the original motion artifact, filtering the motion artifact F to the abnormal shadow candidate region B will completely eliminate the motion artifact and have the same region as the theoretical value. Calculated. However, in reality, since the motion artifact F is estimated as an approximation from the surrounding sampling points Pm, there is no way to know whether or not the obtained motion artifact F is correct.

  Therefore, how close the image quality is to the theoretical value is evaluated. Here, the theoretical value is information on the image sharpness and the standard deviation in the abnormal shadow candidate region B.

As described above, the sharpness is confirmed by placing a sampling point Pm near the contour around the abnormal shadow candidate region B and taking a profile. Examples of the value indicating the sharpness include MTF (Modulation Transfer Function). The sharpness evaluation item H can be calculated by the following equation (7), where MTF _i is the theoretical value MTF _i , MTF _{P is} before filtering, and MTF _a is after filtering.

  The closer the value of H is to “1”, the closer to the theoretical value. That is, the reliability of the motion artifact F obtained by estimation is high, and the possibility of the motion artifact F being included in the abnormal shadow candidate region B is high.

The standard deviation is evaluated only when the abnormal shadow candidate region B is a homogeneous substance. X-ray projection (forward projection) of an object with material properties equivalent to an abnormal shadow candidate is performed by simulation, image reconstruction is performed, and the standard deviation of the object is measured. The theoretical value obtained in this way is defined as SD _i . As shown in FIG. 15, for the standard deviation in the abnormal shadow candidate region B, the evaluation value I of the standard deviation can be calculated by the following equation (8), with SD _p before filtering and SD _a after filtering.

  The closer the value of I is to “1”, the closer to the theoretical value. That is, the reliability of the motion artifact F obtained by estimation is high, and the possibility of the motion artifact F being included in the abnormal shadow candidate region B is high.

The CPU 101 calculates the _second evaluation term p ₂ regarding the possibility that the motion artifact F is included in the abnormal shadow candidate region B from the evaluation in step S404 (step S405).

And evaluating terms G sharpness, define what took the sum of evaluation term I of the standard deviation as a second evaluating terms p _2. The maximum value is 1 (sharpness) +1 (standard deviation) = 2, which is defined as p _2max .

Thereafter, the CPU 101 evaluates the possibility that an artifact component is included in the abnormal shadow candidate region B based on the first evaluation term p ₁ obtained in step S402 and the second evaluation term p ₂ obtained in step S405. Information p is obtained (step S406). The evaluation information p is obtained in the form of probability (step S406). The evaluation information p is defined by the following equation (9).

Here, w _1, w ₂ is the weight factor for each of the evaluating terms p _1, p _2, shows a ratio to trust each evaluating terms p _1, p _2. w ₁ + w ₂ = 1.

  When the evaluation information p is calculated, the CPU 101 outputs the calculated evaluation information (step S105 in FIG. 2). The output is, for example, storage in the main memory 102 or the storage device 103, display on the display device 107, and the like.

  FIG. 16 shows an example of a user interface 8 suitable for the present invention.

  The user interface 8 is a screen that receives input of a plurality of sampling points Pm set when estimating a motion artifact F existing in the abnormal shadow candidate region B and displays calculated evaluation information.

  In the user interface 8, a CT image 3 of the subject is displayed and a tool box 81 is provided. In the tool box 81, a “sampling point P arrangement selection part selection” button 82 is arranged. In the example of FIG. 16, the CT image 3 shows an example in which one axial image is displayed. However, the CT image 3 may not be an axial image, and may be a three-dimensional image such as volume rendering. The number of images may be plural.

  As shown in FIG. 16B, the sampling point P can be arranged on the CT image 3 when the “sampling point P arrangement selection of assumed part” button 82 is pressed. The CPU 101 accepts the arrangement of the sampling points P by the user and displays the arranged sampling points in a list 83 as a list. The CPU 101 recognizes the material property of the tissue at each sampling point P from the pixel value of the CT image 3 and displays it together with the sampling point P.

  Alternatively, the user may input the material property of the tissue at the sampling point P. When the user sets a plurality of sampling points P, the CPU 101 starts the artifact estimation process in step S103 and estimates the motion artifact F of the abnormal shadow candidate region B. The estimated motion artifact is displayed in the “artifact strength F” column 83 b of the list 83.

  In addition, the CPU 101 executes the evaluation information calculation process in step S104. The generated evaluation information is displayed in the “accuracy” column 83 c of the list 83. The estimation of the motion artifact F and the calculation of the evaluation information may be performed in real time together with the arrangement of the sampling points, or may be performed after all the sampling points are arranged.

  FIG. 16B shows a display state of the user interface 8 at a stage where two sampling points are arranged, and FIG. 16C shows a stage where six sampling points are arranged.

  As shown in Fig. 16B, when the number of sampling points is 2, the artifact strength is calculated as "+ 4mm in the AP direction" and the accuracy is calculated as "10%". However, as shown in Fig. 16C, sampling is performed. When the number of points is 6, the strength of the artifact is calculated as “+5 mm in the AP direction”, and the accuracy is “60%”.

  As described above, the image processing apparatus 100 extracts the abnormal shadow candidate area B from the input image data, and estimates the motion artifact F existing in the extracted abnormal shadow candidate area B from the surrounding artifact components. Then, evaluation information p regarding the possibility that the estimated motion artifact F is included in the abnormal shadow candidate region B is calculated and output.

  As a result, the user can easily grasp how reliable the extracted abnormal shadow candidate is. The motion artifact F of the abnormal shadow candidate region B is extracted because it is inferred from the artifact components V1 to Vn (strength and direction) at a plurality of points P1 to Pn set in the contour portion where the influence of motion is likely to appear in the image. The component of motion (motion artifact F) included in the region B can be obtained with high accuracy. For example, it is possible to evaluate the validity of an abnormal shadow candidate extracted at a position close to an image where breath holding has failed or a part including a large amount of movement such as the heart, and support diagnosis by a doctor or the like.

  The preferred embodiments of the image processing apparatus and the like according to the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the technical idea disclosed in the present application, and these are naturally within the technical scope of the present invention. Is done.

  1 Image processing system, 100 Image processing device, 101 CPU 101, 102 Main memory, 103 Storage device, 104 Communication I / F, 105 Display memory, 106 I / F, 107 Display device, 108 Mouse, 109 Input device, 110 Network, 111 Image database, 112 Medical imaging device, 3 CT image, 6 objects, 61 Forward projection data, 62 Theoretical image, 71, 71p Tangent line of contour, 72, 72p Line segment straddling contour, B Abnormal shadow candidate area, C, D profile, E filter, F motion artifact, P1-Pn, Pm Sampling point, Q1-Qn, Qm Line segment connecting coordinates BX of abnormal shadow candidate area and points P1-Pn, Pm, R1-Rn, Rm Vector from abnormal shadow candidate region B to points P1 to Pn and Pm, V1 to Vn Artifact components at sampling points P1 to Pn

Claims (6)

An input unit for inputting image data;
An extraction unit that extracts abnormal shadow candidate regions from the input image data;
An artifact estimation unit that estimates motion artifacts present in the abnormal shadow candidate region;
An evaluation information calculation unit that calculates evaluation information about the possibility that the motion artifact is included in the abnormal shadow candidate region;
An output unit for outputting the evaluation information;
An image processing apparatus comprising:   The artifact estimation unit obtains an artifact component at a plurality of points set in a contour portion around the abnormal shadow candidate region, and estimates a motion artifact of the abnormal shadow candidate region by integrating the artifact components of each point 2. The image processing apparatus according to claim 1, wherein:   The artifact estimation unit generates a theoretical image, obtains a profile of the contour for each of the input image data and the theoretical image, and estimates the artifact component based on the degree of matching of the profiles The image processing device according to claim 2. The evaluation information includes the position information of the contour portion around the abnormal shadow candidate region, or the first evaluation term for evaluating the size of the motion artifact, and the abnormal shadow candidate region using the estimated motion artifact. Including a second evaluation term based on image quality when provisionally corrected,
The position information includes at least one evaluation item among validity, number, and distribution of positions for a plurality of points set in a contour portion around the abnormal shadow candidate region when calculating the motion artifact. The image processing apparatus according to claim 1 .   The apparatus includes a user interface that receives input of a plurality of points set when estimating a motion artifact existing in the abnormal shadow candidate region and displays the evaluation information calculated by the evaluation information calculation unit. Item 12. The image processing device according to Item 1. An image processing method in an image diagnostic apparatus,
Inputting image data;
Extracting an abnormal shadow candidate region from the input image data;
Inferring motion artifacts present in the abnormal shadow candidate region;
Calculating evaluation information about the possibility that the motion artifact is included in the abnormal shadow candidate region;
Outputting the evaluation information;
An image processing method comprising:

Images