Optic Disc Detection

This code detects blood vessels and optic discs in retinal images. Effective optic disc detection in color retinal imagery serves as a fundamental step in automated retinal image analysis systems. The proposed methodology implements a novel approach for automatic optic disc localization and precise boundary detection. The algorithm utilizes iterative thresholding followed by connected component analysis to approximate the optic disc center, then applies a geometric model based on implicit active contour models to define exact boundaries. Validation on a curated database of 148 retinal images demonstrates 99.3% localization accuracy, with boundary detection achieving sensitivity of 90.67±5% and specificity of 94.06±5% compared to human expert annotations.

Retinal Blood Vessel Extraction Using Kirsch's Edge Detection Templates

This program implements blood vessel extraction from retinal images using Kirsch's edge detection templates. The algorithm applies Kirsch's templates at multiple orientations to filter input retinal images, with an adjustable threshold parameter for fine-tuning the vessel extraction output. The implementation includes directional filtering and morphological processing to enhance vascular structure detection.

3D Vascular Image Reconstruction Using MATLAB

Implementing 3D vascular image reconstruction with MATLAB, leveraging its powerful image processing capabilities for handling large datasets through specialized algorithms and visualization tools