29 August 2016 A new feature selection method for the detection of architectural distortion in mammographic images
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003341 (2016) https://doi.org/10.1117/12.2244633
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Architecture distortion is one of the most common signs of breast cancer in mammograms, and it is difficult to detect due to its subtlety. Computer-Aided Diagnosis (CAD) technology has been widely used for the detection and diagnosis of breast cancer. In this paper, Gabor filters and phase portrait analysis are used to locate suspicious regions based on the image characteristic of architectural distortion. Twin bounded Support Vector Machine (TWSVM), a kind of binary classifier, is employed reduce the large amounts of false positives. In this paper, we proposed a novel feature selection which is based on Multiple Twin Bound Support Vector Machines Recursive Feature Elimination (MTWSVM-RFE). The results showed that our proposed method detect the region of architecture distortion with high accuracy.
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Xiaoming Liu, Leilei Zhai, Ting Zhu, Kai Zhang, "A new feature selection method for the detection of architectural distortion in mammographic images", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003341 (29 August 2016); doi: 10.1117/12.2244633; https://doi.org/10.1117/12.2244633
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