AN EFFICIENT TECHNIQUE FOR RETINAL VESSEL SEGMENTATION AND DENOISING USING MODIFIED ISODATA AND CLAHE

Khan Bahadar Khan,Muhammad Shahid,Amir A Khaliq,Sheroz Khan

doi:10.31436/iiumej.v17i2.611

Abstract

Retinal damage caused due to complications of diabetes is known as Diabetic Retinopathy (DR). In this case, the vision is obscured due to the damage of retinal tinny blood vessels of the retina. These tinny blood vessels may cause leakage which affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is essential for analysis of DR. Automatic blood vessels segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature most of the people have used computationally hungry a strong preprocessing steps followed by a simple thresholding and post processing, But in our proposed technique we utilize an arrangement ofÂ light pre-processing which consists of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, a difference image of green channel from its Gaussian blur filtered image to remove local noise or geometrical object, Modified Iterative Self Organizing Data Analysis Technique (MISODATA) for segmentation of vessel and non-vessel pixels based on global and local thresholding, and a strongÂ post processing using region properties (area, eccentricity) to eliminate the unwanted region/segment, non-vessel pixels and noise that never been used to reject misclassified foreground pixels. The strategy is tested on the publically accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases. The performance of proposed technique is assessed comprehensively and the acquired accuracy, robustness, low complexity and high efficiency and very less computational time that make the method an efficient tool for automatic retinal image analysis. Proposed technique perform well as compared to the existing strategies on the online available databases in term of accuracy, sensitivity, specificity, false positive rate, true positive rate and area under receiver operating characteristic (ROC) curve.

Highlights

Retinal blood vessel segmentation mainly focuses on the presence of the Diabetic Retinopathy (DR) illness
Retinal anomalies are commonly caused by DR and hypertensive retinopathy, which are the significant reasons for visual deficiency and impairment these days
The segmentation of the first observer has been utilized as a ground truth for assessment while the performance of the second observer has been used as a benchmark for comparison

Summary

Introduction

Retinal blood vessel segmentation mainly focuses on the presence of the DR illness. Retinal anomalies are commonly caused by DR and hypertensive retinopathy, which are the significant reasons for visual deficiency and impairment these days. Fundus imaging is progressively used as a part of the screening process. The retinal vein elements like microaneurysms, scratching, and narrowing have been connected to the systemic sickness of retinopathy in its earliest stages [1]. Visual deficiency usually caused by DR is irreversible even a recuperation procedure would not enable the patient to have earlier vision capacity. The early identification of DR on a retinal image will save the patient from having irreversible visual impairment

Methods

Results

Conclusion