TY - JFULL AU - Birmohan Singh and V. K. Jain PY - 2015/8/ TI - Computer Aided Classification of Architectural Distortion in Mammograms Using Texture Features T2 - International Journal of Computer and Information Engineering SP - 1734 EP - 1740 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10002270 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 103, 2015 N2 - Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Architectural distortions, masses and microcalcifications are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support vector machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and an accuracy of 96% for mammogram images collected from digital database for screening mammography database. ER -