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Open AccessArticle

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

College of Engineering, Huaqiao University, Quanzhou 362021, China
School of medical, Huaqiao University, Quanzhou 362021, China
Fujian Provincial Big Data Research Institute of Intelligent Manufacturing, Huaqiao University, Quanzhou 362021, China
College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Author to whom correspondence should be addressed.
Algorithms 2019, 12(7), 135;
Received: 26 April 2019 / Revised: 15 June 2019 / Accepted: 27 June 2019 / Published: 30 June 2019
(This article belongs to the Special Issue Algorithms for Computer-Aided Design)
PDF [1934 KB, uploaded 3 July 2019]


Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value. View Full-Text
Keywords: computer-aided diagnosis; mammography; Contourlet; adaptive support vector machine; classifier computer-aided diagnosis; mammography; Contourlet; adaptive support vector machine; classifier

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Cai, S.; Liu, P.-Z.; Luo, Y.-M.; Du, Y.-Z.; Tang, J.-N. Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM. Algorithms 2019, 12, 135.

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