基于平稳小波能量特征提取的乳腺微钙化检测与分类

Badhan Mazumder, S. Islam, Md. Moshiur Rahman, M. Nurullah
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引用次数: 2

摘要

由于放射科医生广泛使用乳房x线微钙化作为乳腺癌筛查的初始工具,准确检测微钙化是建立有效诊断系统的必经阶段。提出了一种基于平稳小波变换(SWT)的乳腺微钙化检测与分类新方法。为了检测乳房x线照片中可疑的微钙化,在多个水平上应用SWT进行分解,然后利用平稳小波能量(SWE)对每个获得的详细SWT系数子带进行特征提取。使用四种不同的集成分类器根据这些SWE特征将微钙化分类为良性或恶性,进行10倍交叉验证。采用MIAS数据库进行实验评估,采用子空间判别集成分类器获得的灵敏度为94.12%,特异度为92.48%,精密度为88.89%,准确度为92.11%。此外,比较分析的结果证明了我们提出的方法优于两种最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stationary Wavelet Based Energy Feature Extraction for Detection and Classification of Mammographic Microcalcifications
Since radiologists widely use mammographic microcalcifications as initial tool for breast cancer screening, accurate detection of microcalcifications is an inevitable stage to develop an effective diagnosis system. This paper proposes a Stationary Wavelet Transform (SWT) based novel technique for detection and classification of breast microcalcifications. To detect the suspected microcalcifications from mammograms, SWT was applied at multiple levels for decomposition purpose and Stationary Wavelet Energy (SWE) was then implemented for feature extraction from each obtained detailed SWT coefficient sub-bands. Four different Ensemble classifiers were employed for classification of microcalcifications as benign or malignant using these SWE features, conducting 10 fold cross validation. Mammographic Image Analysis Society (MIAS) mammographic database was used for experimental evaluation and at maximum a sensitivity of 94.12%, a specificity of 92.48%, a precision of 88.89% and an accuracy of 92.11% were obtained using Subspace Discriminant Ensemble classifier. Beside outcomes of comparative analysis prove the supremacy of our proposed approach over two state-of-the-art approaches.
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