A computerised framework for characterisation of breast tissue using mammographic images Online publication date: Thu, 22-Aug-2019
by Indrajeet Kumar; Jitendra Virmani; H.S. Bhadauria
International Journal of Computational Systems Engineering (IJCSYSE), Vol. 5, No. 4, 2019
Abstract: In this study various experiments have been conducted for four-class and two-class breast tissue pattern characterisation using various texture feature models in transform domain. These experiments have been conducted on 480 mammographic images taken from the DDSM dataset. From each image, ROI of predefined size 128 × 128 pixels has been extracted from the core location of the breast tissue where remarkable amount of glandular tissues are found. Various statistical parameters have been computed from each ROI using transform domain texture feature models. The computed texture feature vectors are passed to the classification phase. The classification phase is consisted of SVM classifier. From the exhaustive experiments conducted in this study, it can be concluded that the maximum accuracy of 86.2% has been obtained for four-class breast tissue characterisation and the maximum accuracy of 91.2% has been obtained for two-class breast tissue characterisation.
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