Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images
Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images
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DOI：
10.1109/tmi.2012.2230403
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发表时间：
2013-07
影响因子：
10.6
通讯作者：
W. Moon;Yi-Wei Shen;M. Bae;Chiun-Sheng Huang;J. Chen;R. Chang
中科院分区：
文献类型：
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作者：
W. Moon;Yi-Wei Shen;M. Bae;Chiun-Sheng Huang;J. Chen;R. Chang
全自动全乳房超声(ABUS)是一种新兴的乳房异常筛查工具。本研究开发了一种基于多尺度斑点检测的计算机辅助检测(CADE)系统，用于分析ABUS图像。使用136个乳腺病变(58个良性病变和78个恶性病变)和37个正常病例的数据库对所提出的CADE系统的性能进行了测试。在斑点噪声去除后，采用多尺度斑点检测的Hessian分析方法对肿瘤进行检测。这种方法可以检测到所有的肿瘤，但也可以检测到一些非肿瘤。使用基于模糊、内部回声和形态特征的Logistic回归模型来评估其余候选患者的肿瘤可能性。肿瘤可能性高于特定阈值(0.4)的候选肿瘤被认为是肿瘤。通过结合模糊、内部回声和形态特征与10倍交叉验证，所提出的CAD系统的灵敏度分别为100%、90%和70%，每次通过的假阳性分别为17.4、8.8和2.7。我们的结果表明，基于多尺度斑点检测的CADE系统可以用于检测ABUS图像中的乳腺肿瘤。
Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor likelihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.