FIREFLY ALGORITHM BASED MAMMOGRAPHIC IMAGE ANALYSIS

Abstract

Mammography images are used to detect tumor formation within breast tissues. It works as a monitoring stage where a medical practitioner judges that the probability of cancer. Several image processing techniques in conjunction with various algorithms shows ways to enhance and detect large tissue lumps. But the domain of new nature inspired metaheuristic algorithms remains untouched. Generally these algorithm are meant to solve optimization problems but this study attempts to involve one of the algorithms which is known as Firefly algorithm, to enhance the quality of mammographic image by enhancing the edge details and contrast. As this shown, next the enhanced result is segmented using self organizing map (SOM) to properly visualize the large tissue lump formation within breast, which is then clustered using K-means algorithm. The results of enhancement also shown via pick signal to noise ratio and histogram difference. Whereas segmentation results are presented for visual analysis only, because of the reason that mammographic images needs to be analyzed by medical practitioners as appears on screen.