Adaptive Gabor Filtering using Grey Wolf Optimization for Enhancement of Brain MRI

During last few decades, brain tumor becomes the 10th most dominant origin of fatality among the men, women and children. Regular screening, early detection and proper treatment arrangements are very effective in lessening the mortality rate. Poor visibility of medical images misguides the radiologist to detect the prognosis of cancer. In this view, present study proposes a fast and automated image enhancement approach by introducing a novel aspects of contrast enhancement of brain MRI by addressing grey wolf optimization (GWO) based Gabor filtering (GF). GWO hierarchically searches the optimum parameters of GF efficiently in terms of the proposed fitness function. The filtered image is able to capture high frequency intensity regions (edges/curvatures). Dynamic contrast of filtered image is corrected by automated power-law transformation (PLT). Performances of the proposed methodology are analyzed qualitatively and quantitatively in compare to other conventional image enhancement techniques. Since the proposed approach is able to improve the visual clarity of suspicious region of brain MRI so it performs superior to other techniques. The result of the proposed method is enterprising with very low computational time and accuracy of 95.1%.