MRI Medical Image Enhancement using Modulated Intensity Gradient and Texture Gradient Based Segmentation

Abstract

Medical image processing has experienced dramatic expansion and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use of various imaging modalities, more challenges arise; for example, how to process and analyze a significant volume of images so that high quality information can be produced for disease diagnoses and treatment. The principal objectives of this work is to develop a MRI medical image enhancement system to analyze and extract more information from the image so as to better diagnose the disease and also to promote interests for further study and research in medical imaging processing. In image processing different processes are applied on images to enhance it at different parameters. These processes are not directly applied on images but before that it needs to be segmented or divided in smaller parts called pixels or small blocks of pixels. So segmentation becomes an integral and basic part in image analysis and error at this stage can influence other processing techniques. The performance of proposed system is compared with the available techniques. The proposed system enhances the Mean Square Error (MSE), Maximum Error (MAXERR) and Peak Signal to Noise Ratio (PSNR) parameters.