Detection of microcalcification clusters in digitized X-ray mammograms using unsharp masking and image statistics

Authors : Pelin KUŞ, İrfan KARAGÖZ

Pages : 2048-2061

Doi:10.3906/elk-1202-114

View : 15 | Download : 5

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :A fully automated method for detecting microcalcification insert ignore into journalissuearticles values(MC); clusters in regions of interest insert ignore into journalissuearticles values(ROIs); extracted from digitized X-ray mammograms is proposed. In the first stage, an unsharp masking is used to perform the contrast enhancement of the MCs. In the second stage, the ROIs are decomposed into a 2-level contourlet representation and the reconstruction is obtained by eliminating the low-frequency subband in the second level. In the third stage, statistical textural features are extracted from the ROIs and they are classified using support vector machines. To test the performance of the method, 57 ROIs selected from the Mammographic Image Analysis Society`s MiniMammogram database are used. The true positive and false positive rates are used to evaluate the performance of the classification, and the results are compared with those from other studies presented in the literature. The results show that the classification method of unsharp masking and low-band eliminated image statistics is convenient for MC cluster detection. In particular, a true positive rate of about 94% is achieved at the rate of 0.06 false positives per image.
Keywords : Image analysis, microcalcification detection, mammography, Mammographic Image Analysis Society database, classification, support vector machines