Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms
Mammogram images have the ability to assist physicians in detecting breast cancer caused by cells abnormal growth. But due to visual interpretation, false results can be obtained. In this paper, to reduce false results, image segmentation is carried out to find breast cancer mass. Image segmentation using Fuzzy clustering: K means, FCM, and FPCM shows result better than other existing methods but initialization problem and sensitivity to noise do not make them to achieve better accuracy. Various extension of the FCM for segmentation is developed. But most of them modify the objective function which changes the basic FCM algorithm. Hence efforts have been made to develop FCM algorithm without modifying objective function for better segmentation. We have proposed a technique GA-ACO-FCM, which is the hybridization of optimization tools: genetic algorithm and ant colony optimization with fuzzy C means .GA-ACO-FCM is suitable to overcome initialization problem of FCM and shows better results with achieving high accuracy.
KeywordsImage segmentation Mammography K-means Fuzzy C-means FPCM and GA-ACO-FCM
The authors thank to Prof. A.K. Tripathy, Dr. B.B. Mishra, and Dept. of Electronics and Telecommunication for their valuable tips and suggestion on this topic and programming.
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