Modified Fuzzy C Means with Optimized Ant Colony Algorithm for Image Segmentation

Segmentation technique is used for analysis and diagnosis of tumor in MRI Brain image. The paper presents Modified Fuzzy C Means with Optimized Ant Colony Algorithm for segmentation of brain tumors in 3D magnetic resonance images. In this paper, the image segmentation is obtained by using Modified Fuzzy C Means with Optimized Ant Colony Algorithm using Min - Max Ant System. The proposed methodology is tested on 13 real patients MRI images. It is compared with Fuzzy C Means and Fuzzy C Means with Ant Colony Algorithm. The Fuzzy C Means algorithm is not efficient due to limitation in initialization. FCM with ACA has the disadvantage of requirement of increased processing time. The proposed methodology shows a PSNR improvement of 9.33 dB and 0.62 dB over FCM and FCM with ACA respectively. It has segmentation accuracy improvement of 24.55% and 0.22% over FCM and FCM with ACA respectively. It shows 55.72 sec reduction in processing time over FCM with ACA.