Computer-aided diagnosis of breast cancer via Gabor wavelet bank and binary-class SVM in mammographic images
Breast cancer is one of the most dangerous diseases that attack women in their 40s worldwide. Due to this fact, it is estimated that one in eight women will develop a malignant carcinoma during their life. In addition, the carelessness of performing regular screenings is an important
reason for the increase of mortality. However, computer-aided diagnosis systems attempt to enhance the quality of mammograms as well as the detection of early signs related to the disease. In this paper we propose a bank of Gabor filters to calculate the mean, standard deviation, skewness
and kurtosis features by four-sized evaluation windows. Therefore, an active strategy is used to select the most relevant pixels. Finally, a supervised classification stage using two-class support vector machines is utilised through an accurate estimation of kernel parameters. In order to
show the development of our methodology based on mammographic image analysis, two main experiments are fulfilled: abnormal/normal breast tissue classification and the ability to detect the different breast cancer types. Moreover, the public screen–film mini-MIAS database is compared
with a digitised breast cancer database to evaluate the method robustness. The area under the receiver operating characteristic curve is used to measure the performance of the method. Furthermore, both confusion matrix and accuracy are calculated to assess the results of the proposed algorithm.
Keywords: Gabor filters; SVM; X-ray images; kernels; kurtosis; mean; skewness; standard deviation; types of breast cancer
Document Type: Research Article
Affiliations: 1: Department of Computer Engineering and Mathematics, University Rovira i Virgili, Tarragona, Spain 2: Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands
Publication date: 03 March 2016
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