MR Brain Image Segmentation Optimized by Using Ant Colony Algorithm

In the last decades, image processing has become an interesting task in the field of medical imaging. one of the most important medical imaging techniques used for diagnosis is the magnetic resonance image (MRI) which is considered to be a very useful medical tool for detecting the tumor progression of multiple sclerosis MS. The segmentation of brain tumors from MRI images is an effective technique. Several recent approaches for segmentation and classification of MRI sequences have been proposed for the automatic detection of MS outliers. This paper illustrates an algorithm based on the meta-heuristic “Ant Colonies Optimization ACO” for the segmentation of MRI images. We propose to apply the technique of ant colonies to estimate the segmentations of brain MRI images from a novel MR datasets of 30 MS patients, which were acquired with a 3T MR scanner with conventional sequences in order to optimize their overall rendering and to compare with consensual segmentation. The submitted algorithm is evaluated by using MATLAB GUI program.