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Controlled Target-Density Histogram Matching for Brightness-Preserving Contrast Enhancement in Medical Images

by Eyad Abu-Sirhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 103
Year of Publication: 2026
Authors: Eyad Abu-Sirhan
10.5120/ijca5f9a182cff42

Eyad Abu-Sirhan . Controlled Target-Density Histogram Matching for Brightness-Preserving Contrast Enhancement in Medical Images. International Journal of Computer Applications. 187, 103 ( May 2026), 26-31. DOI=10.5120/ijca5f9a182cff42

@article{ 10.5120/ijca5f9a182cff42,
author = { Eyad Abu-Sirhan },
title = { Controlled Target-Density Histogram Matching for Brightness-Preserving Contrast Enhancement in Medical Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2026 },
volume = { 187 },
number = { 103 },
month = { May },
year = { 2026 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number103/controlled-target-density-histogram-matching-for-brightness-preserving-contrast-enhancement-in-medical-images/ },
doi = { 10.5120/ijca5f9a182cff42 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-17T02:29:11+05:30
%A Eyad Abu-Sirhan
%T Controlled Target-Density Histogram Matching for Brightness-Preserving Contrast Enhancement in Medical Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 103
%P 26-31
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a brightness-preserving histogram matching framework for grayscale image enhancement with emphasis on medical imaging applications. A parametric center-shifted U-shaped target density is introduced to control intensity redistribution and reduce brightness distortion. Closed-form expressions for normalization and output mean are derived, enabling analytical control of enhancement behavior through minimization of the Absolute Mean Brightness Error (AMBE). Experimental results demonstrate that the proposed method achieves effective contrast enhancement with lower brightness distortion compared with Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). An adaptive local extension (LU-EM) is also proposed to improve spatial adaptability and local detail enhancement. The results confirm that the proposed framework provides stable enhancement with effective brightness preservation for medical images.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Contrast Enhancement; Histogram Matching; Histogram Equalization; CLAHE; Absolute Mean Brightness Error (AMBE); Medical Image Processing; Target Density; U-Shaped Distribution

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