Region Entropy–Based Histogram Equalization for Medical Image Contrast Enhancement

Abstract

Contrast enhancement is applied in image processing and visualization as a preprocessing step to improve image clarity prior to visual inspection, object detection, and image segmentation. In medical imaging, contrast enhancement plays an important role in emphasizing regions of interest. However, existing algorithms often scatter pixel intensities in a histogram, leading to noise amplification, over-saturation, and poor human perception. To overcome these limitations, Region Entropy-based Histogram Equalization (REHE) was introduced as a preprocessing algorithm to enhance the local contrast while preserving structural integrity and texture information. The proposed algorithm is evaluated on publicly available multimodal medical images and benchmarked against multiple state-of-the-art enhancement algorithms. Results show that the proposed approach improves image quality and structural preservation, leading to better visual and diagnostic outcomes.