Detection of Cancer Cells in Gabour Filtered Mammogram Using Gray Level Co-Occurrence Matrices

This paper presents a hybrid technique which aims to assist radiologist in identifying breast cancer at its earlier stages through mammograms. It is difficult to identify masses in raw mammogram. Hence, in this paper an intelligent system is designed to diagnose breast cancer in mammogram using intelligent techniques such as Gabor filter and gray level co-occurrence matrices. Preprocessing, Segmentation and mass extraction are the three major steps involved in the proposed method. In preprocessing, down sampling and quantization is applied on input mammogram, following it noise removal is efficiently performed using median filter and finally Region of Interest is extracted using histogram matching. In segmentation, a band-pass filter is formed by rotating a 1-D Gaussian filter(off center) in frequency space, termed as-Circular Gaussian Filter (CGF). A CGF can be uniquely characterized by specifying a central frequency and a frequency band. Usually mass appears as a brighter region on a mammogram. Mass region can be segmented out using a threshold that is adaptively decided upon the histogram analysis of the CGF-filtered mammogram. Finally extraction of masses is performed using gray level co-occurrence matrices (GLCM) features. GLCM Features like entropy, contrast and homogeneity is analyzed in order to detect whether extracted region contains masses or normal tissue. Efficiency of the proposed method is calculated by analyzing true positive, true negative and false positive, false negative results. Receiver Operating Characteristics curve method is used to analyze efficiency of the proposed method. Thus, the proposed approach would be helpful for automated real time breast cancer diagnosis.