Open AccessANALYSIS OF MAMMOGRAM FOR DETECTION OF BREAST CANCER USING WAVELET STATISTICAL FEATURES
Author(s) -
Sk. Nowshad,
Umar Farooq
Publication year - 2012
Publication title -
international journal of image processing and vision sciences
Language(s) - English
Resource type - Journals
ISSN - 2278-1110
DOI - 10.47893/ijipvs.2012.1016
Subject(s) - gabor filter , computer science , artificial intelligence , wavelet , pattern recognition (psychology) , breast cancer , gabor wavelet , mammography , feature (linguistics) , feature extraction , digital mammography , region of interest , computer vision , wavelet transform , cancer , discrete wavelet transform , medicine , linguistics , philosophy
Early detection of breast cancer increases the survival rate and increases the treatment options. One of the most powerful techniques for early detection of breast cancer is based on digital mammogram. A system can be developed for assisting the analysis of digital mammograms using log-Gabor wavelet statistical features. The proposed system involves three major steps called Pre-processing, Processing, and Feature extraction. In pre-processing, the digital mammogram can be de-noised using efficient decision-based algorithm. In processing stage, the suspicious Region of Interest (ROI) can be cropped and convolved with log-Gabor filter for four different orientations. Then gray level co-occurrence matrix (GLCM)can be constructed for log-Gabor filter output at four different orientations and from that first order statistical features and second order statistical features can be extracted to analyze whether the mammogram as normal or benign or malignant. The proposed method can allow the radiologist to focus rapidly on the relevant parts of the mammogram and it can increase the effectiveness and efficiency of radiology clinics.