Chapter 4 - Efficient Medical Image Enhancement Technique Using Transform HSV Space and Adaptive Histogram Equalization

Abstract

Medical imaging is required for medical analysis and to extract information about various parts of the body. Imaging methods such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and X-ray are used for diagnosis, and they suffer from low contrast leading to deterioration of image quality. Image enhancement is a technique to improve the perception of information in images so as to provide better visualization. The Medical Image Enhancement plays a vital role and targets the problems of low contrast and high-level noise in accurate diagnosis of particular disease and also for research documentation and analysis. In this work, we propose Efficient Medical Image Enhancement using Transform (Hue, Saturation, and Value) HSV Space, and Adaptive Histogram Equalization. The input color image is converted from RGB to transform HSV space while enhancing only the S space with enhancement factor. The S and V spaces are subjected to Adaptive Histogram Equalization with calculation of local variance for both. Further, the correlation between V and S space is calculated with luminance enhancement saturation feedback. Finally, the Enhanced Luminance V and S Space with H space are converted back to RGB to obtain the enhanced image. The gray images are subjected to the same procedure using Adaptive Histogram Equalization along with pre- and postprocessing filters while excluding the conversion space. The standard medical images from standard datasets (MEDPIX) are considered and subjected to analysis and validation. It is observed that the proposed method is better compared to existing methods in terms of PSNR and also the enhanced quality.