Enhancing Conjunctival Vasculature Imaging: A Multi-Objective Cuckoo Search Approach for Contrast Enhancement...

Area of Science:

• Ophthalmology
• Biomedical Imaging
• Image Processing

Background:

• Conjunctival imaging is crucial for diagnosing various eye conditions.
• Enhancing the quality of vascular conjunctival images is essential for accurate analysis.
• Current methods may struggle with optimizing contrast and noise reduction simultaneously.

Purpose of the Study:

• To develop and evaluate an automated method for enhancing vascular conjunctival images.
• To optimize image contrast and minimize noise amplification using a novel approach.
• To improve the overall quality of conjunctival vasculature images for diagnostic purposes.

Main Methods:

• Conjunctival images were acquired using a slit lamp biomicroscope.
• Contrast Limited Adaptive Histogram Equalization (CLAHE) was used for enhancement.
• Multi-Objective Cuckoo Search (MOCS) optimization algorithm tuned CLAHE parameters to maximize contrast and minimize noise.

Main Results:

• Both CLAHE and MOCS-CLAHE enhanced image contrast.
• MOCS-CLAHE provided superior overall vessel visibility and image quality compared to standard CLAHE.
• Quantitative metrics (PSNR, SSIM, NIQE) confirmed the improved performance of MOCS-CLAHE.

Conclusions:

• MOCS optimization is an efficient method for tuning CLAHE parameters in conjunctival imaging.
• The MOCS-CLAHE approach yields high-quality images with emphasized vessel structures.
• This technique effectively balances increased contrast with minimal noise amplification.