A support vector machine approach for detection of microcalcifications
A support vector machine approach for detection of microcalcifications
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DOI：
10.1109/tmi.2002.806569
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发表时间：
2002-12-01
影响因子：
10.6
通讯作者：
Nishikawa, RM
中科院分区：
文献类型：
--
作者：
El-Naqa, I;Yang, YY;Nishikawa, RM
在本文中，我们研究了一种基于支持向量机（SVMs）的方法来检测微钙化（MC）集群在数字乳腺X线照片，并提出了一个连续的增强学习计划，以提高性能。SVM是一种基于结构风险最小化原则的机器学习方法，当应用于训练集之外的数据时表现良好。我们制定MC检测作为一个监督学习问题，并应用支持向量机开发检测算法。我们使用SVM检测在图像中的每个位置是否存在MC。我们使用包含1120个MC的76个临床乳房X线照片的数据库测试了所提出的方法。我们使用自由响应的接收机工作特性曲线来评估检测性能，并将所提出的算法与现有的几种方法进行比较。在我们的实验中，所提出的SVM框架优于所有其他测试方法。特别是，高达94%的灵敏度，实现了由SVM方法在一个假阳性聚类每图像的错误率。支持向量机的能力优于几个众所周知的方法开发的广泛研究的问题MC检测表明，支持向量机是一个很有前途的技术，在医学成像应用中的对象检测。
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to outperform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.