Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms
Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms
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DOI：
10.1109/tbme.2005.862536
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发表时间：
2006-02-01
影响因子：
4.6
通讯作者：
Namba, K
中科院分区：
文献类型：
--
作者：
Nakayama, R;Uchiyama, Y;Namba, K
乳房x光检查被认为是早期发现乳腺癌最有效的方法。然而，放射科医生很难检测到微钙化团簇。因此，我们开发了一种计算机方案，用于在乳房x光检查中检测早期微钙化簇。我们首先基于Hessian矩阵的概念开发了一种新的滤波器组，用于分类结节结构和线性结构。该滤波器组将乳房x线图像按1 ~ 4的尺度分解为若干个子图像进行二次差分。然后通过对Hessian矩阵的分析得到结节分量（NC）子图像和结节和线性分量（NLC）子图像。从乳房x线图像中选择许多感兴趣区域（roi）。在每个ROI中，从尺度为1到4的NC子图像和尺度为1到4的NLC子图像中确定8个特征。采用贝叶斯判别函数区分具有微钙化簇的异常roi和不具有微钙化簇的两种不同类型的正常roi。我们通过使用600张乳房x光片来评估检测性能。我们的计算机方案被证明具有检测微钙化簇的潜力，具有临床可接受的灵敏度和低假阳性。
Mammography is considered the most effective method for early detection of breast cancers. However, it is difficult for radiologists to detect microcalcification clusters. Therefore, we have developed a computerized scheme for detecting early-stage microcalcification clusters in mammograms. We first developed a novel filter bank based on the concept of the Hessian matrix for classifying nodular structures and linear structures. The mammogram images were decomposed into several subimages for second difference at scales from 1 to 4 by this filter bank. The subimages for the nodular component (NC) and the subimages for the nodular and linear component (NLC) were then obtained from analysis of the Hessian matrix. Many regions of interest (ROIs) were selected from the mammogram image. In each ROI, eight features were determined from the subimages for NC at scales from I to 4 and the subimages for NLC at scales from 1 to 4. The Bayes discriminant function was employed for distinguishing among abnormal ROIs with a microcalcification cluster and two different types of normal ROIs without a microcalcification cluster. We evaluated the detection performance by using 600 mammograms. Our computerized scheme was shown to have the potential to detect microcalcification clusters with a clinically acceptable sensitivity and low false positives.