Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering. (July 2017)

• Record Type: Journal Article
• Authors: Rodrigues, Luiz Carlos; Marengoni, Maurício
• Is Part Of: Biomedical signal processing and control. Volume 36(2017)
• Page Start: 39
• Page End: 49
• Publication Date: 2017-07
• Subjects: 00-01 -- 99-00; Retinal fundus images -- Mathematical morphology -- Wavelets -- Graphs; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.03.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Graphical abstract: Abstract : Highlights: Optic disc and vessel segmentation in retinal images are proposed. We apply genetic algorithm for parameters optimization. The proposed method does not require pre or post processing. We achieve the accuracy of 0.9465. Our algorithm was tested against two images databases. Abstract: The high importance of the accurate and early diagnostic has motivated the development of computer vision techniques of image processing and segmentation required for an completely automated assessment system for the retinal conditions. In this study we present a new algorithm built on wavelets transforms and mathematical morphology for detecting the optic disc and we explore the tubular characteristic of the blood vessels to segment the retinal veins and arteries. Both, optic disc and vascular structure, are landmarks for image registration and are essential for the retinal image analysis. Instead of a manual try and error method to choose the best parameters for detecting vessels as accurately as possible, we used a genetic algorithm and its sequence of generations and crossovers. However the technique of exploring the tubular characteristic of the vessels reaches its limits when the vessels are represented by, sometimes not continuous, winding lines of 1 pixel. To overcome this limitation we adopted a graph based approach using Dijkstra's shortest path algorithm to track the segments and a statistic method of Student t distribution to assess whether or not the identified segment is part of the vascular structure. The proposed method was developed and tested on the Digital Retinal Images for Vessel Extraction (DRIVE) freely available database, which contains 40 annotated color eye fundus image.