Watershed segmentation for breast tumor in 2-D sonography
Watershed segmentation for breast tumor in 2-D sonography
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DOI：
10.1016/j.ultrasmedbio.2003.12.001
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发表时间：
2004-05-01
影响因子：
2.9
通讯作者：
Chen, DR
中科院分区：
文献类型：
--
作者：
Huang, YL;Chen, DR
使用医学超声（US）成像自动描绘乳腺肿瘤轮廓可以帮助没有相关经验的医生做出正确的诊断。本研究结合了神经网络（NN）分类和形态学分水岭分割的优点，从超声图像中提取乳腺肿瘤的精确轮廓。采用纹理分析为神经网络提供输入以对超声图像进行分类。自协方差系数指定纹理特征，以对 US 使用自组织图 (SOM) 成像的乳房进行分类。对超声检查中的纹理特征进行分类后，SOM 输出将选择自适应预处理程序。最后，分水岭变换自动确定肿瘤的轮廓。在本研究中，使用 60 名患者的图像对所提出的方法进行了训练和测试。计算机模拟的结果表明，所提出的方法总是能识别出与超声图像中乳腺肿瘤的手动轮廓（由经验丰富的医生）获得的轮廓和感兴趣区域（ROI）相似的轮廓和感兴趣区域（ROI）。随着超声成像的日益普及，功能性自动轮廓方法必不可少，其临床应用也变得紧迫。这种方法提供了 US 图像的稳健且快速的自动轮廓绘制。这项研究并不是要强调自动轮廓技术优于手动技术。毕竟，自动和手动轮廓不一定会产生相同的实际病理边界。在计算机辅助诊断（CAD）应用中，自动分割可以节省绘制精确轮廓所需的大量时间，并且具有非常高的稳定性。 (E-mail: ylhuang@mail.thu.edu.tw) (C) 2004 世界超声医学生物学联合会。
Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians without relevant experience, in making correct diagnoses. This study integrates the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. Textural analysis is employed to yield inputs to the NN to classify ultrasonic images. Autocovariance coefficients specify texture features to classify breasts imaged by US using a self-organizing map (SOM). After the texture features in sonography have been classified, an adaptive preprocessing procedure is selected by SOM output. Finally, watershed transformation automatically determines the contours of the tumor. In this study, the proposed method was trained and tested using images from 60 patients. The results of computer simulations reveal that the proposed method always identified similar contours and regions-of-interest (ROIs) to those obtained by manual contouring (by an experienced physician) of the breast tumor in ultrasonic images. As US imaging becomes more widespread, a functional automatic contouring method is essential and its clinical application is becoming urgent. Such a method provides robust and fast automatic contouring of US images. This study is not to emphasize that the automatic contouring technique is superior to the one undertaken manually. Both automatic and manual contours did not, after all, necessarily result in the same factual pathologic border. In computer-aided diagnosis (CAD) applications, automatic segmentation can save much of the time required to sketch a precise contour, with very high stability. (E-mail: ylhuang@mail.thu.edu.tw) (C) 2004 World Federation for Ultrasound in Medicine Biology.