Patent Publication Number: US-9886757-B2

Title: Lesion detecting method and lesion detecting apparatus for breast image in rotating manner

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the priority benefit of Taiwan application serial no. 105102651, filed on Jan. 28, 2016. The entirety of the above-mentioned patent application is hereby incorporated by reference herein and made a part of this specification. 
     BACKGROUND OF THE DISCLOSURE 
     Field of the Disclosure 
     The disclosure relates to a medical image processing technique, and particularly relates to a lesion detecting method and a lesion detecting apparatus for breast image in rotating manner. 
     Description of Related Art 
     Mammary carcinoma is one of the most common malignant tumors in woman, and the main symptoms thereof include breast tumor, abnormal secretions, or shape variation, etc. To early screen the abnormal breast symptoms avails treating the tumor as early as possible, so as to decrease a chance of deterioration or proliferation of cancer cells. Screening methods such as clinical or self breast detection, biopsy, mammography, ultrasound or magnetic resonance imaging, etc., have been widely used in clinical practice or become important issues in academic researches. 
     According to researches, it is known that compared to a low density breast, women with a high density breast has a high risk of suffering from breast cancer. Therefore, density analysis on breast and mammary glandular tissues is also an important factor in breast cancer assessment. On the other hand, although a computer aided detection (CADe) system has been used in clinical practice to automatically identify tumors, bumps or calcifications, it still has a high risk of false positive. 
     SUMMARY OF THE DISCLOSURE 
     The disclosure is directed to a lesion detecting method and a lesion detecting apparatus for breast image in a rotating manner, which efficiently reduces false positive of a computer aided detection system. 
     The disclosure provides a lesion detecting method for breast image in a rotating manner, which at least (but not limited to) following steps. A set of breast image in the rotating manner is obtained. The set of breast mage in the rotating manner contains a plurality of sub breast images. The sub breast images are reconstructed to generate a reconstructed breast image. The reconstructed breast image is compared with the set of breast image in the rotating manner without being reconstructed to confirm at least one lesion position. 
     According to another aspect, the disclosure provides a lesion detecting apparatus, which at least includes (but not limited to) a storage unit and a processing unit. The storage unit records a plurality of modules. The processing unit is coupled to the storage unit, and accesses and executes the modules recorded in the storage unit. The modules include an image input module, an image reconstruction module and a lesion determination module. The image input module obtains a set of breast image in the rotating manner. The set of breast image in the rotating manner contains a plurality of sub breast images. The image reconstruction module reconstructs the sub breast images to generate a reconstructed breast image. The lesion determination module compares the reconstructed breast image with the set of breast image in the rotating manner without being reconstructed. Accordingly, the lesion determination module may confirm at least one lesion position according to the comparing result. 
     According to the above description, in the lesion detecting method and the lesion detecting apparatus for breast image in a rotating manner provided by the embodiments of the disclosure, the breast image in the rotating manner is reconstructed, and the reconstructed breast image is compared with the breast image in the rotating marinerto confirm a lesion (for example, tumor, bump or calcification) position. In this way, the embodiments of the disclosure assist reducing false positive of the computer aided detection system. 
     In order to make the aforementioned and other features and advantages of the disclosure comprehensible, several exemplary embodiments accompanied with figures are described in detail below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification The drawings illustrate embodiments of the disclosure and, together with the description, serve to explain the principles of the disclosure. 
         FIG. 1  is a block diagram of a lesion detecting apparatus according to an embodiment of the disclosure. 
         FIG. 2  is a flowchart illustrating a lesion detecting method for breast image in a rotating manner according to an embodiment of the disclosure. 
         FIG. 3  is an example of rotatory scanning. 
         FIG. 4  is a schematic diagram of a breast image in the rotating manner in a three-dimensional space. 
         FIG. 5A - FIG. 5C  are schematic diagrams of a fill-up operation according to an embodiment of the disclosure. 
         FIG. 6  is a flowchart illustrating an automatic lesion detecting method according to an embodiment of the disclosure. 
         FIG. 7A  is a partial image of a breast image in the rotating manner without being reconstructed. 
         FIG. 7B  shows a region segment image. 
         FIG. 7C  shows a suspicious lesion region determined according to the region segment image of  FIG. 7B . 
         FIG. 7D  shows a lesion position of  FIG. 7C  screened through the false-positive reduction operation. 
         FIG. 8  is a schematic diagram of determining a thickness region. 
         FIG. 9A  is an example of a projection image. 
         FIG. 9B  is an example of a segmented projection image. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
       FIG. 1  is a block diagram of a lesion detecting apparatus according to an embodiment of the disclosure. Referring to  FIG. 1 , the lesion detecting apparatus  100  at least includes (but not limited to) a storage unit  110  and a processing unit  150 . The lesion detecting apparatus  100  can be an electronic apparatus such as a server, a user device, a desktop computer, a notebook, a network computer, a working station, a personal digital assistant (PDA), a personal computer (PC), a computer aided detection (CADe) system, etc., which is not limited by the disclosure. 
     The storage unit  110  can be any type of a fixed or movable random access memory (RAM), a read-only memory (ROM), a flash memory or a similar device or a combination of the aforementioned devices. In the present embodiment, the storage unit  110  is used for storing a breast image in a rotating manner, sub breast images, a reconstructed breast image, scanning parameters, a program code, a device configuration, buffer or permanent data, and records software programs such as an image input module  111 , an image reconstruction module  113 , a lesion detennination module  115 , and an image quality module  117 . Operation details of the above modules are described later in following embodiments. The storage unit  110  of the present embodiment is not limited to be a single memory device, and the aforementioned software modules can also be separately stored in two or more memory devices of the same type or different types. 
     Functions of the processing unit  150  can be implemented by using a programmable unit such as a central processing unit (CPU), a microprocessor, a micro controller, a digital signal processing (DSP) chip, a field programmable gate array (FPGA), etc. The functions of the processing unit  150  can be implemented by using an independent electronic device or integrated circuit (IC), and the processing unit  150  can also be implemented in a hardware or software manner. 
     In order to facilitate understanding an operation flow of the embodiment of the disclosure, a plurality of embodiments is provided below to describe a flow that the lesion detecting apparatus  100  of the present embodiment performs breast image processing and lesion detection in detail.  FIG. 2  is a flowchart illustrating a lesion detecting method for breast image in a rotating manner according to an embodiment of the disclosure. Referring to  FIG. 2 , the method of the present embodiment is adapted to the lesion detecting apparatus  100  of  FIG. 1 . The method of the present embodiment is described below with reference of various components and modules of the lesion detecting apparatus  100 . Various steps of the method can be adjusted according to an actual implementation requirement, which is not limited by the disclosure. 
     In step S 210 , the image input module  111  obtains a set of breast image in the rotating manner. The set of breast image in the rotating manner contains a plurality of (or at least one slice of) sub breast images. In the present embodiment, the sub breast images are respectively obtained through a scanner by circling under a breast image taking container for one circle to implement rotatory scanning. The scanner, for example, has a probe based on a medical image scanning technique such as automated breast ultrasound (ABUS), digital breast tomosynthesis (DBT), magnetic resonance imaging. (MRI), etc. Regarding the ultrasound scanning, the breast image taking container can be loaded with liquid or water-soluble ointment to serve as ultrasonic transmission media. 
     For example,  FIG. 3  is an example of rotatory scanning. Referring to  FIG. 3 , a breast  301  of a user can be completely or partially disposed in a cylindrical breast image taking container  320  as the user lays down in a prone position. A movable scanner  340  with probe(s) is disposed under the bottom of the (fixed) cylindrical breast image taking container  320 , and a mechanical device (not shown) can be used to drive the scanner  340  to rotate by at least one circle (360°) along a rotation direction RD 1  (i.e. clockwise) or a direction opposite to the rotation direction RD 1  (i.e. anticlockwise), such that a scanning range may cover all of or a part of a projection area of the breast  301 . During the scanning process, the scanner  340  scans the breast  301  to obtain one sub breast image by every rotation angle (for example, 3°, 5°, 8°, etc.). For example, the rotation angle is 3°, and the scanner  340  rotates by one circle) 360°) to obtain  120  sub breast images. 
     It should be noted that before the rotatory scanning is performed, scanning parameters can be preset or manually adjusted. The scanning parameters at least include (but not limited to) an image scanning start position, a rotation direction (clockwise or anticlockwise), a rotation angle, etc., and can be recorded in the storage unit  110  for subsequent use. 
     The image input module  111  may obtain the breast image in the rotating manner from the storage unit  110 , through a wireless or wired communication unit (for example, Wi-Fi, Ethernet), directly through a medical image scanner (for example, an ABUS scanner, a MRI scanner, etc.) of  FIG. 3 , or from a storage device (for example, a DVD, a flash drive, a hard disk, etc.). 
     In step S 230 , the image reconstruction module  113  reconstructs the sub breast images to generate a reconstructed breast image. In order to facilitate viewing the breast image obtained through the rotating manner according to different viewing angles, in the embodiment of the disclosure, the reconstruction is performed according to rotation characteristics of the rotatory scanning. 
     In an embodiment, the image reconstruction module  113  transforms the sub breast images into an image set according to the scanning start position, the rotation angle and the rotation direction of the scanner, and determines a missing position of each gap between two adjacent sub breast images in the image set, and complement the mission position through an interpolation method. 
     To be specific,  FIG. 4  is a schematic diagram of a breast image in the rotating manner in a three-dimensional space. For simplicity&#39;s sake, only two sub breast images  431  and  432  in  FIG. 4  are taken as an example for description, though the disclosure is not limited thereto. A width W of each sub breast image is a rotation radius (for example, a length that the scanner  340  of  FIG. 3  can scan one time), and a height H thereof is a maximum depth that can be scanned by the scanner. The image reconstruction module  113  sequentially arranges each of the sub breast images (for example, the sub breast images  431  and  432 ) obtained through one circle of the rotatory scanning in the respective scanning positions according to the recorded or predetermined scanning parameters (for example, the image scanning start position, the clockwise or anticlockwise rotation, the rotation angle, etc.), so as to form a cylinder  410 . Now, in the cylinder  410 , the two sub breast images  431  and  432  have a gap (i.e. do not have pixels or scanning images) there between. 
     Then, the image reconstruction module  113  performs an operation to fill up the gaps.  FIG. 5A - FIG. 5C  are schematic diagrams of a fill-up operation according to an embodiment of the disclosure. Referring to  FIG. 5A , the image reconstruction module  113  establishes a two-dimensional (2D) Cartesian coordinate system for the cylinder  410  formed by a plurality of the sub breast images in  FIG. 4 . For example, the 2D Cartesian coordinate system of a plane where the round top of the cylinder  410  is located. 
     The image reconstruction module  113  may transform the 2D Cartesian coordinate system into a coordinate system represented by a rotation angle and a radius length. To be specific, coordinates of a separation position  501  of two adjacent sub breast images in the Cartesian coordinate system are defined as (x 1 , y 2 ) (a center point of the round top of the cylinder  410  is taken as an origin (0,0) and an a connection line of the image scanning start position and the center point of the round top is an x-axis (or referred to as a horizontal axis)), and a diameter width Width (i.e. 2*W) of the round top of the cylinder  410  and a height Height (i.e. H) are defined. Then, the image reconstruction module  113  transforms the coordinates (x 1 , y 1 ) according to following equations (1) (Pythagorean theorem) and (2) to obtain coordinates (r 1 , θ 1 ): 
     
       
         
           
             
               
                 
                   
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     Where, r 1  represents a distance between the position and the center point of the round top of the cylinder  410 , and θ 1  represents a rotation angle started from the scanning start position. 
     According to the aforementioned transformation, the image reconstruction module  113  maps a part of or all of the sub breast images to the image set. Referring to  FIG. 5B , a horizontal axis of such coordinate system is rotation angle θ (started from the scanning start position), and a vertical axis is the radius r. The sub breast images are sequentially arranged on the r-θ coordinate system along the rotation direction, and are reconstructed to form an image set  530 . For example, if the rotation direction RD 1  in  FIG. 4  is clockwise, the sub breast image  431  in the image set is then located to the left of the sub breast image  432 . The separation position  501  in  FIG. 5A  can also be mapped to a missing position  503  in the image set  530 . 
       FIG. 5C  is partial enlarged view of the image set  530 . The image reconstruction module  113  can complement the mission position  503  through bilinear interpolation, bicubic interpolation, nearest interpolation, etc. After the gaps of all of the adjacent sub breast images are complemented, the integral image set (for example, without missing position) is formed. The image reconstruction module  113  may transform the image set from the r-θ coordinate system back to the presentation of  FIG. 5A  to form a 3D reconstructed breast image. The reconstructed breast image does not have the gap between two adjacent sub breast images shown in  FIG. 4 . 
     The processing unit  150  may further display the reconstructed breast image through a display unit (not shown, for example, a liquid crystal display (LCD), a plasma display panel (PDP), an organic light emitting diode (OLED), etc.), and may receive an input operation of the user through an input unit (not shown, for example, a touch device, a keyboard, a mouse, etc.), so as to inspect the reconstructed breast image via different viewing angles. 
     In step S 250 , the lesion determination module  115  compares the reconstructed breast image with the set of breast image in the rotating manner without being reconstructed to confirm at least one lesion position. In the present embodiment, a region-based lesion (for example, tumor, bump or calcification, etc.) detecting method is used to automatically detect the breast image in the rotating manner. According to the provided regional screening method, the suspicious lesion region is conditionally screened to find out the lesion position. 
       FIG. 6  is a flowchart illustrating an automatic lesion detecting method according to an embodiment of the disclosure. Referring to  FIG. 6 , in step S 610 , the lesion determination module  115  performs region segment to the breast image in the rotating manner without being reconstructed to generate a region segment image. The lesion determination module  115  may use an original image (i.e. a slice of sub breast image in the breast image in the rotating manner without being reconstructed) to perform pixel-based or texture difference-based region segment (for example, watershed segment, Markov random field (MRF) segment). 
     For example,  FIG. 7A  is a partial image of the breast image in the rotating manner without being reconstructed. It is assumed that the breast image in the rotating manner  710  has a tumor  711 . After the lesion determination module  115  performs the region segment to the breast image in the rotating manner  710 , a region segment image  730  shown in  FIG. 7B  is produced. In the region segment image  730 , the pixels having the same or similar texture characteristics are divided into a same region (indicated by a same color). It should be noted that various parameters in the region segment algorithm can be adjusted according to an actual requirement, which is not limited by the disclosure. 
     In step S 630 , the lesion determination module  115  determines at least one suspicious lesion region in the region segment image. To be specific, after the lesion determination module  115  segments the breast image in the rotating manner, the region segment image still includes a plenty of unnecessary blocks, and the target lesion block is also included therein. The lesion determination module  115  may perform preliminary screening according to a target characteristic (for example, a darker region, an approximate ellipse, a long-short axis ratio) to be detected by using pixel characteristics of each block (for example, an average value, the maximum value, the minimum value, a median value, a variance, etc.), so as to determine the suspicious lesion region(s), where the characteristics and features of each block of the image can be adjusted according to an actual requirement. For example,  FIG. 7C  shows a suspicious lesion region  731  determined according to the region segment image  730  of  FIG. 7B . 
     In step S 650 , the lesion determination module  115  may perform a false-positive reduction operation to the suspicious lesion region to determine a lesion position. To be specific, after the preliminary screening, the lesion determination module  115  further performs false-positive reduction on the remained blocks by using the screened suspicious lesion blocks. The characteristics used by the false-positive reduction may at least include (but not limited to) three parts: shape (for example, an area, a long-short axis ratio), pixel strength (for example, an average value, a standard deviation) and texture (for example, gray-level co-occurrence matrix (GLCM), Markov random field (MRF), or Gabor filter). In other words, the lesion determination module  115  may screen the remained blocks according to the predetermined characteristics or manually selected characteristics to find out the target lesion position(s). For example,  FIG. 7D  shows a lesion position  733  of  FIG. 7C  screened through the false-positive reduction operation. 
     In step S 670 , the lesion determination module  115  determines whether a connected lesion region exists in the reconstructed breast image according to the lesion position, so as to confirm the lesion position(s). To be specific, the lesion determination module  115  compares the determined lesion position (for example, the lesion position  733  in  FIG. 7D ) with the 3D reconstructed breast image. If the 3D reconstructed breast image has the connected lesion region (i.e. a region corresponding to the lesion position), it represents that the lesion actually exists (or an existence chance thereof is higher than 80%, 90%, etc.) If he 3D reconstructed breast image does not have the connected lesion region, it represents that the lesion does not exist (or the existence chance thereof is lower than 10%, 15%, etc.). 
     In some embodiments, the processing unit  150  may further present one of prompt massages of finding lesion (for example, “find tumor!”), the lesion position, the suspicious lesion region, the lesion region, etc., or a combination thereof through the display unit, so as to assist the medical staff to clearly learn the inspection situation. 
     Moreover, in order to maintain quality control of the image scanning, in some embodiments, the image quality module  117  further determines whether the breast image in the rotating manner is complete or a shooting error thereof is too high (for example, an error rate is greater than 70%, 80%, etc.). The image quality module  117  may perform a vertical projection on an image of a partial thickness region in the breast image in the rotating manner to generate a projection image, and determines the image quality of the breast image in the rotating manner according to a ratio between a shooting error type and a skin tissue type in the projection image. 
     To be specific, after the step S 210  or S 230  or before the step S 250 , the processing unit  150  determines whether to perform subsequent lesion detection according to a result of the image quality determined by the image quality module  117 . The image quality module  117  may determine the thickness region of different thickness value (for example, 2 cm, 5 cm, etc., which is varied along with different users) according to the 3D reconstructed breast image or the sub breast image set forming the cylinder  410  of  FIG. 4 . 
     For example,  FIG. 8  is a schematic diagram of determining the thickness region. The image quality module  117  determines a thickness value TH according to a top part of the sub breast image set forming the cylinder  810  or the reconstructed breast image, so as to determine a thickness region  811 . 
     The image quality module  117  then performs the vertical projection on the image of the determined thickness region (for example, the thickness region  811  of  FIG. 8 ), where the lowest pixel value of all of the pixels along the vertical direction in each of the positions can be taken as a value of each of the aforementioned positions. 
     The image quality module  117  may further remove an unnecessary region at the periphery of the skin tissue. For example,  FIG. 9A  is an example of a projection image. Referring to  FIG. 9A , the image outside a target block  911  in the projection image  910  is considered to be the unnecessary region. 
     The image quality module  117  adopts the region segment method (for example, the watershed segment, MRF segment, etc.) to divide the projection image into a skin tissue type block and a shooting error type block according to the pixel characteristics of the target region. For example,  FIG. 9B  is an example of the segmented projection image. Referring to  FIG. 9B , the segmented projection image  920  includes a skin tissue type block  921  and shooting error type blocks  923 . The image quality module  117  may generate a ratio (for example, a total area of the shooting error type blocks  923  divided by a total area of the skin tissue type block  921 ) according to areas of the two types of blocks to determine a level of the image scanning quality. For example, if the ratio is greater than a quality threshold (for example, 30%, 20%, etc.), the image quality module  117  determines that the image scanning quality is poor, and the scanning operation is re-performed. For example, a prompt message “rescan is recommended!” is displayed through the display unit. Conversely, if the ratio is smaller than the quality threshold (for example, 15%, 30%, etc.), the image quality module  117  determines that the image scanning quality is good, and the lesion detection operation of the step S 250  can be performed. 
     In summary, in the lesion detecting method and the lesion detecting apparatus for breast image in a rotating manner provided by the embodiments of the disclosure, the breast image in the rotating manner is reconstructed to facilitate the medical staff to inspect the same in different viewing directions. By using the region-based lesion detecting method, the lesion position can be screened, and can be compared with the 3D reconstructed breast image to reduce false-positive. Moreover, in the embodiment of the disclosure, the image scanning quality can be maintained though the image quality determination. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the disclosure without departing from the scope or spirit of the disclosure. In view of the foregoing, it is intended that the disclosure cover modifications and variations of this disclosure provided they fall within the scope of the following claims and their equivalents.