MRI Image Enhancement: Optimized Filtering Mechanism for Achieving High Accuracy in Diagnose Process

Abstract

Segmentation plays an important role in separating data from medicinal images and also helps in clinical findings. Segmentation is the way toward apportioning the image into different regions. MRI is utilized to extract images of delicate tissues of human body. It is utilized in analyzing the human organs without the requirement of surgery. For the most part MRI images contain a lot of noise caused by operator performance, equipment and the environment, which prompts genuine errors. MRI is a productive way in giving data in regards to the area of tumors and even the volume. The noise present in the MRI image can be evacuated by utilizing different de-noising procedures whichever is most appropriate method depending on the type of image obtained and afterward can be handled by any of the segmentation techniques. The noise in MRI images might be because of field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. In our proposed paper a review of different noise handling and filtering mechanism is conducted in order to enhance the quality of image. In this paper we modify the adaptive median filter by applying redundancy handling mechanism and enhance the contrast of image by applying histogram equivalence method.