Hybrid UM-LT-AHE Technique for Contrast Enhancement of Medical Images

Abstract

This paper is concerned with combinations of un-sharp masking, logarithmic transformation, and adaptive histogram equalization techniques to arrive at a hybrid method for enhancement of different types of medical image's contrast. Motivation behind the hybridization is the need to have a contrast enhancement method that is not application-specific and that can be deployed to several medical image enhancements. Four different types of medical images: X-ray, ultrasound, magnetic resonance, and computer tomographic images are utilized in the evaluations of the proposed hybrid contrast enhancement method. As performance metrics, absolute mean brightness error, mean square error, peak signal to noise ratio, and entropy are used. Comparative results, both qualitative and quantitative, were conducted at the end of the research, and the proposed method outperformed the other three (CLAHE, fuzzy-based, and wavelet transform-based) related selected methods in the field that used the same dataset in terms of testing accuracy. The enhancement quality of the proposed method was found to be satisfactory and can be used for any time of medical image; thus, the proposed hybrid technique produces better enhanced medical images from different medical image inputs.