MRI Mammogram Image Segmentation using NCut method and Genetic Algorithm with partial filters
Cancer is one of the most common leading deadly diseases which affect men and women around the world. Among the cancer diseases, breast cancer is especially a concern in women. It has become a major health problem in developed and developing countries over the past 50 years and the incidence has increased in recent years. Recent trends in digital image processing are CAD systems, which are computerized tools designed to assist radiologists. Most of these systems are used for automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of breast increases. In this paper , the proposed algorithm uses partial filters to enhance the images and the Ncut method is applied to segment the malignant and benign regions , further genetic algorithm is applied to identify the nipple position followed by bilateral subtraction of the left and the right breast image to cluster the cancerous and non cancerous regions. The system is trained using Back Propagation Neural Network algorithm. Computational efficiency and accuracy of the proposed system are evaluated based on the Frequency Receiver Operating Characteristic curve(FROC). The algorithm are tested on 161 pairs of digitized mammograms from MIAS database. The Receiver Operating Characteristic curve leads to 99.987% accuracy in detection of cancerous masses.
Keywords: Filters, Normalized Cut, Segmentation, BPN, Genetic Algorithm and FROC
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