CLASSIFICATION OF MAMMOGRAMS HAVING ARCHITECTURAL DISTORTION AND SPICULATED MASSES USING TEXTURE FEATURES
Dhanashri Aghor, M.R. Banwaskar · INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH · 2021
Architectural distortion is the third most common mammographic appearance of nonpalpable breast cancer, representing nearly 6% of abnormalities detected on screening mammography. Although its prevalence on mammography is small compared with calcication or visible mass, architectural distortion is also more difcult to diagnose because it can be subtle and variable in presentation. Early detection of breast cancer is possible by nding architectural distortion in monographic images. Spiculated masses account for about 14% of biopsied lesions and about 81% of these are malignant. Current CAD systems are dramatically better at detecting microcalcications than masses. The sensitivity is considerably lower for Spiculated Masses that are rated as "subtle" by radiologists Moreover, since current systems were devised with masses and calcications in mind, they don’t perform as well on other, less prevalent but still clinically signicant lesion types. In this paper, we propose a computer aided diagnosis system for distinguishing abnormal mammograms with architectural distortion or spiculated masses from normal mammograms. Five types of texture features GLCM, GLRLM, fractal texture, spectral texture and HOG features for the regions of suspicion are extracted. Support vector machine has been used as classier in this work. The proposed system yielded an overall accuracy of 97.29% for mammogram images collected from mini-MIAS database which is better as compared to existing methods.