Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree

TL;DR

This paper presents a fast, simple method for detecting diabetic retinopathy features in retinal images, achieving a mean recall of 90.03% by analyzing connected components based on morphological constraints.

Contribution

It introduces a novel morphological cascading decision tree approach for efficient diabetic retinopathy screening in retinal images.

Findings

Mean recall of 90.03% on test images

Effective detection of diabetic retinopathy features

Comparison with ground truth validates accuracy

Abstract

This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages, exudates, vessels, optic disc or anything else. Then we apply some constraints such as compactness, area of blob, intensity and contrast for screening of candidate connectedcomponent responsible for diabetic retinopathy. We obtain our final results by doing some post processing. The results are compared with ground truths. Performance is measured by finding the recall (sensitivity). We took 10 images of dimension 500 * 752. The mean recall is 90.03%.