Bat Optimized Watershed based Segmentation of Lamina Cribrosa

TL;DR

This paper introduces a novel segmentation method for Lamina Cribrosa using wavelet transform, bat optimization, and watershed algorithm, achieving high accuracy in detecting glaucomatous damage.

Contribution

The paper presents a new bat-optimized watershed segmentation approach for LC that improves accuracy over existing methods.

Findings

Achieved 99.29% segmentation accuracy.

Enhanced segmentation quality with bat optimization.

Improved detection of glaucomatous damage.

Abstract

The segmentation of Lamina Cribrosa(LC) is a challenging task to detect the glaucomatous damage. In this paper a new method of segmenting the LC using bat optimized Watershed segmentation is done. By using wavelet transform LC structures are decomposed. Then, the decomposed image is optimized using Bat algorithm and by applying histogram equalization the optimized image is normalized. Watershed algorithm is used to segment the Lamina Cribrosa from its outer layer. Using some parameters like PSNR, MSE, F-Measure, rand index, sensitivity, specificity, SSIM and accuracy, the performance of the proposed system is calculated. The results show that the proposed method provides higher accuracy of 99.29%.