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A metaheuristics-equipped post-processing model for coronary angiograms.

K Y Devi¹, J S Bobby², S Vinurajkumar³

¹Department of Biomedical Engineering, Easwari Engineering College, Ramapuram, Chennai, Tamil Nadu 600089, India.

Radiography (London, England : 1995)

|February 21, 2026

Summary

This summary is machine-generated.

This study introduces an optimized Contrast-Limited Adaptive Histogram Equalization (CLAHE) method for enhancing coronary angiograms. The proposed Optimum CLAHE (OCLAHE) method improves image quality and aids in stenosis detection.

Keywords:

Contrast-limited adaptive histogram equalization Coronary angiograms Grey wolf optimization Metaheuristics Patch-based contrast quality index

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Area of Science:

Medical Imaging
Image Processing
Cardiovascular Diagnostics

Background:

Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhances coronary angiograms.
Optimizing the clip limit (CL) is essential for high-quality, distortion-free CLAHE outputs.

Purpose of the Study:

To automate clip limit (CL) selection for CLAHE in coronary angiogram enhancement.
To evaluate the performance of the optimized CLAHE (OCLAHE) against existing methods.

Main Methods:

Developed a parameter optimization model integrating Grey Wolf Optimization (GWO) and a patch-based contrast quality index (PCQI) fitness function.
Compared OCLAHE with Histogram Equalization (HE), Unsharp Masking (USM), Morphological Filtering (MF), and Frangi Filter (FF).
Assessed image quality using subjective visual appeal and objective Image Quality Assessment (IQA) indices: PCQI, Quality-Aware Relative Contrast Measure (QRCM), entropy, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE).

Main Results:

OCLAHE demonstrated superior performance across all objective IQA indices compared to HE, USM, MF, and FF.
OCLAHE achieved higher PCQI, QRCM, and entropy values.
OCLAHE resulted in a lower BRISQUE score, indicating enhanced visual quality.

Conclusions:

OCLAHE preserves gradient profiles and textural integrity (high QRCM), maintains high information content and contrast (high entropy), and presents a natural appearance (low BRISQUE).
OCLAHE effectively amplifies intensity differences between vessels and background without over-amplifying overall brightness.
The enhanced visual quality from OCLAHE facilitates both automated and subjective detection of stenosis in coronary angiograms.