文章基本信息

标题：Mass Lesion Detection Using Wavelet Decomposition Transform and Support Vector Machine
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作者：Ayman AbuBaker
期刊名称：International Journal of Computer Science & Information Technology (IJCSIT)
印刷版ISSN：0975-4660
电子版ISSN：0975-3826
出版年度：2012
卷号：4
期号：2
页码：33
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：This paper describes the ongoing efforts by the author to provide efficient and accurate classification formass lesions in mammogram images. A study of the characteristics of true masses compared to the falselydetected masses is carried out using wavelet decomposition transform combining with support vectormachine (SVM). In this approach, four main wavelet features are extracted from different regions ofinterest in order to distinguish between TP and FP detected regions. A study of detecting regions ofinterest, extracting the wavelet features and choosing the optimal learning parameters for support vectormachine are also presented in this paper. The combined between the wavelet features and SVM presentedhere can successfully reduces the FP ratio to 0.05 clusters/image, with accurate TP ratio 94%.
关键词：mammogram; mass lesions; wavelet transform; support vector machine.