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首页> 外文期刊>Journal of Digital Imaging >Computerized Segmentation Method for Individual Calcifications Within Clustered Microcalcifications While Maintaining Their Shapes on Magnification Mammograms
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Computerized Segmentation Method for Individual Calcifications Within Clustered Microcalcifications While Maintaining Their Shapes on Magnification Mammograms

机译：集群化微钙化中单个钙化的计算机化分割方法，同时在放大的乳房X光照片上保持其形状

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In a computer-aided diagnosis (CADx) scheme for evaluating the likelihood of malignancy of clustered microcalcifications on mammograms, it is necessary to segment individual calcifications correctly. The purpose of this study was to develop a computerized segmentation method for individual calcifications with various sizes while maintaining their shapes in the CADx schemes. Our database consisted of 96 magnification mammograms with 96 clustered microcalcifications. In our proposed method, a mammogram image was decomposed into horizontal subimages, vertical subimages, and diagonal subimages for a second difference at scales 1 to 4 by using a filter bank. The enhanced subimages for nodular components (NCs) and the enhanced subimages for both nodular and linear components (NLCs) were obtained from analysis of a Hessian matrix composed of the pixel values in those subimages for the second difference at each scale. At each pixel, eight objective features were given by pixel values in the subimages for NCs at scales 1 to 4 and the subimages for NLCs at scales 1 to 4. An artificial neural network with the eight objective features was employed to enhance calcifications on magnification mammograms. Calcifications were finally segmented by applying a gray-level thresholding technique to the enhanced image for calcifications. With the proposed method, a sensitivity of calcifications within clustered microcalcifications and the number of false positives per image were 96.5% (603/625) and 1.69, respectively. The average shape accuracy for segmented calcifications was also 91.4%. The proposed method with high sensitivity of calcifications while maintaining their shapes would be useful in the CADx schemes.
机译：在计算机辅助诊断（CADx）方案中，用于评估乳房X线照片上成簇的微钙化的恶性可能性，有必要正确分割单个钙化。这项研究的目的是开发一种计算机化的分割方法，用于将各种大小的钙化同时保留在CADx方案中的形状。我们的数据库由96个放大的乳房X线照片和96个成簇的微钙化组成。在我们提出的方法中，通过使用滤镜组，将乳房X射线照片图像分解为水平子图像，垂直子图像和对角线子图像，以获得第1到第4比例的第二个差异。结节成分（NCs）的增强子图像和结节和线性成分（NLCs）的增强子图像是通过对每个比例的第二个差异的那些子图像中的像素值组成的Hessian矩阵进行分析而获得的。在每个像素处，由比例为1-4的NC子图像和比例为1-4的NLC的子图像中的像素值给出八个目标特征。采用具有八个目标特征的人工神经网络来增强放大X线照片的钙化。最后，通过对增强图像应用灰度阈值技术对钙化进行分割。使用所提出的方法，在簇状微钙化中钙化的敏感性和每个图像的假阳性数分别为96.5％（603/625）和1.69。分段钙化的平均形状准确度也为91.4％。所提出的具有高敏感性钙化同时保持其形状的方法将在CADx方案中有用。

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