X-ray Images Enhancement Based on Fuzzy Membership Functions

Abstract

The main objective of image enhancement techniques is to process and improve the image quality and the visual appearance to the human viewer also improving diagnostic viewing in case of Medical Images. Image can be enhanced in various ways such as contrast enhancement, intensity, density slicing, edge enhancement, removal of noise, and saturation transformation. Contrast enhancement is a vital part of various fields, such as X-ray image analysis, biomedical image analysis, machine vision (Image Contrast is the difference in appearance of two or more parts of an image seen simultaneously) and Fuzzy Image Enhancement is based on gray level mapping into fuzzy plane, using a membership transform function. Objective of our thesis is to introduce the membership function which is used by basic approach (three triangular member ship function) comparing with another four proposed approaches when different number and many kinds of membership functions are used and modified (trimf,gaussmf, pimf, zmf, gauss2mf, smf, trapmf) for enhancing three cases of x ray images (normal and over and under exposed dose) using fuzzy logic depend on if-then rules system. PSNR, MSE, and RMSE are calculated addition to histogram. Experimental results show that the proposed method (nine gaussmf) can enhance these X ray images better than the basic approach (three trimf).