Citation

Ang, Jinger and Sim, Kok Swee and Tan, Shing Chiang and Lim, Chee Peng (2025) Adaptive Contrast Enhancement With Lesion Focusing (ACELF). IEEE Access, 13. pp. 41785-41796. ISSN 2169-3536

Text Adaptive Contrast Enhancement With Lesion Focusing (ACELF).pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

This study introduces an innovative image processing algorithm, namely Adaptive Contrast Enhancement with Lesion Focusing (ACELF), which is aimed at enhancing the visualization of brain lesions images. Despite the advancements in medical imaging technologies, the early detection and accurate visualization of brain anomalies remain challenging due to limitations in contrast and detail resolution of current imaging techniques. ACELF addresses these challenges by employing advanced processing strategies to improve the clarity and contrast of brain lesions while preserving the overall integrity of the image. This technique combines intensity threshold-based segmentation, a custom histogram matching algorithm for contrast enhancement, and gamma correction for brightness adjustment in non-lesioned areas. Lesion identification is refined through pixel intensity comparison against standard brain tissue norms, while lesion visibility is further enhanced by modulating intensity based on calculated scaling factors. Additionally, entropy scaling is applied to local regions of the image, ensuring complexity is appropriately managed to optimize contrast. The algorithm demonstrated superior performance in Entropy, Enhancement Measure Estimation (EME), and Contrast Improvement Index (CII), validating its effectiveness in enhancing medical image quality and image contrast.

Item Type:	Article
Uncontrolled Keywords:	Gamma, histogram equalization
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Mar 2025 00:32
Last Modified:	28 Mar 2025 00:32
URII:	http://shdl.mmu.edu.my/id/eprint/13628

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