Enhancing MRI Brain Images Using Contourlet Transform and Adaptive Histogram Equalization

Abstract

Human brain can be viewed using MRI images. These images will be useful for physicians, only if their quality is good. We propose a new method called, Contourlet Based Two Stage Adaptive Histogram Equalization (CBTSA), that uses Nonsubsampled Contourlet Transform (NSCT) for smoothing images and adaptive histogram equalization (AHE), under two occasions, called stages, for enhancement of the low contrast MRI images. The given MRI image is fragmented into equal sized sub-images and NSCT is applied to each of the sub-images. AHE is imposed on each resultant sub-image. All processed images are merged and AHE is applied again to the merged image. The clarity of the output image obtained by our method has outperformed the output image produced by traditional methods. The quality was measured and compared using criteria like, Entropy, Absolute Mean Brightness Error (AMBE) and Peak Signal to Noise Ratio (PSNR).