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首页> 外文期刊>IEEE Signal Processing Magazine >Fast detection of masses in computer-aided mammography
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Fast detection of masses in computer-aided mammography

机译：在计算机辅助乳腺摄影中快速检测肿块

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摘要

We present a complete method for fast detection of circumscribed mass in mammograms employing a radial basis function neural network (RBFNN). This method can distinguish between tumorous and healthy tissue among various parenchyma tissue patterns, making a decision whether a mammogram is normal or not, and then detecting the masses' position by performing sub-image windowing analysis. In the latter case, with the implementation of a set of criteria, square regions containing the masses are marked as regions of suspicion (ROS). Fast feature extraction significantly reduces the overall processing time, allowing implementation of the method on low-cost PCs. A detailed description of the proposed method is given. The computational efficiency of the feature extraction module and the neural classifier is derived, followed by an analysis of the effects of various factors such as the size of the window shifting step, the digitization quality, and the mammogram size on the computational effort. In addition, the data set and experimental results are presented.
机译：我们提出了一种完整的方法，用于使用径向基函数神经网络（RBFNN）快速检测乳房X光照片中的外接质量。该方法可以在各种实质组织模式之间区分肿瘤组织和健康组织，确定乳房X线照片是否正常，然后通过执行子图像窗口分析来检测肿块的位置。在后一种情况下，通过执行一组标准，将包含肿块的正方形区域标记为可疑区域（ROS）。快速特征提取大大减少了整体处理时间，从而允许在低成本PC上实施该方法。给出了所提出方法的详细描述。得出特征提取模块和神经分类器的计算效率，然后分析各种因素的影响，例如窗移动步长的大小，数字化质量和乳房X射线照片的大小对计算量的影响。此外，还提供了数据集和实验结果。

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