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首页> 外文期刊>Journal of Computers >MCs Detection with Combined Image Features and Twin Support Vector Machines
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MCs Detection with Combined Image Features and Twin Support Vector Machines

机译：MCS检测组合图像特征和双支持向量机

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—Breast cancer is a common form of cancer diagnosed in women. Clustered microcalcifications(MCs) in mammograms is one of the important early sign. Their accurate detection is a key problem in computer aided detection (CDAe). In this paper, a novel approach based on the recently developed machine learning technique - twin support vector machines (TWSVM) to detect MCs in mammograms. The ground truth of MCs in mammograms is assumed to be known as a priori. First each MCs is preprocessed by using a simple artifact removal filter and a high-pass filter. Then the combined image feature extractors are employed to extract 164 image features. In the combined feature domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and the trained TWSVM is used as a classifier to make decision for the presence of MCs or not. A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm. Experimental results show that the proposed TWSVM classifier is more advantageous for real-time processing of MCs in mammograms.
机译：-Empred癌症是患有妇女患者的常见形式的癌症。乳房X光线照片中的聚类微钙化（MCS）是重要的早期标志之一。它们的准确检测是计算机辅助检测（CDAE）中的关键问题。本文采用了一种基于最近开发的机器学习技术的新方法 - 双支持向量机（TWSVM）以检测乳房X光图中的MCS。假设乳房X线图中MCS的基础事实被称为先验。首先通过使用简单的伪影去除滤波器和高通滤波器预处理每个MC。然后，使用组合的图像特征提取器来提取164个图像特征。在组合特征域中，MCS检测过程被制定为监督学习和分类问题，并且训练的TWSVM用作分类器，以便在存在MCS的情况下做出决定。大量的实验进行评价和比较建议的司仪检测算法的性能。实验结果表明，该TWSVM分类是在乳房X线照片的MC实时处理更有利。

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