Computer Aided Diagnosis System for Mammogram Abnormality

In this chapter, computer aided detection and diagnosis for Breast tumor detection and classification in Mammography is proposed. The proposed system based on four phases. In the first phase, mammogram image is segmented to sub images with size 64 × 64 pixel then high intensity value of pixel is defined in this sub image and specifies this intensity as a seed point to region growing (RG) algorithm which used to specify the ROI. In the second phase, texture features were extracted using gray-level co-occurrence matrix (GLCM) and combined with shape features to characterize region of interest (ROI) to normal, benign or malignant. In the third phase, malignant ROIs are diagnosed and specified to aided doctor for decision taking. Finally, different methods for evaluating classifier are used using confusion matrix, kappa coefficient and response receiver operating characteristic curve (ROC). The effectiveness of the proposed system was measured using 322 mammogram images from the mammographic image analysis society (MIAS) database. From experimental results show that, the accuracy obtained from ROC curve analysis is AUC 94 % with standard error 0.11. The experimental results shows that the proposed system can accurately segment the breast region in a large range of digitized mammograms.

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