Microaneurysms detection using Blob analysis for diabetic retinopathy

Abstract

Blob analysis is a mathematical method to find the region of interest (ROI) by focusing on the characteristics like brightness or colour. In this work, the process to segment Microaneurysms (MAs) involves two stages, which are pre-processing and segmentation. Pre-processing is a phase for noise removal and illumination correction. In this work, several methods were utilized namely Contrast Limited Adaptive Histogram Equalization (CLAHE), Normalization for contrast enhancement and median filter for noise removal. Then, continue with segmentation phase to segment the MAs from the image. In segmentation phase, several methods were used namely morphological opening, thresholding, Hessian Matrix 2D and Eigenvalue of Hessian Matrix. Finally all the resulting images were compared with the benchmark image to measure the accuracy and grading the stage of Diabetic Retinopathy (DR) by comparing the number of detected MAs. The segmentation accuracy of this project is 68% and 55% accuracy for stage grading.