RESEARCH ARTICLE


Ripplet-Transform-based Cycle Spinning Denoising and Fuzzy-CLA Segmentation of Retinal Images for Accurate Hard Exudates and Lesion Detection



Hadi Chahkandi Nejad1, *, Mohsen Farshad2, Tahereh Farhadian3, Roghayeh Hosseini4
1 Department of Electrical Engineering, Birjand Branch, Islamic Azad University, Birjand, Iran
2 Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
3 Faculty of Medical Sciences, Birjand Branch, Islamic Azad University, Birjand, Iran
4 Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran


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© 2019 Nejad et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Electrical Engineering, Islamic Azad University Birjand, Birjand, Iran;
Tel: +989155623083; E-mail: Hchahkandin@iaubir.ac.ir


Abstract

Aims:

Digital retinal images are commonly used for hard exudates and lesion detection. These images are rarely noiseless and therefore before any further processing they should be underwent noise removal.

Background:

An efficient segmentation method is then needed to detect and discern the lesions from the retinal area.

Objective:

In this paper, a hybrid method is presented for digital retinal image processing for diagnosis and screening purposes. The aim of this study is to present a supervised/semi-supervised approach for exudate detection in fundus images and also to analyze the method to find the optimum structure.

Methods:

Ripplet transform and cycle spinning method is first used to remove the noises and artifacts.

Results:

The noises may be normal or any other commonly occurring forms such as salt and pepper. The image is transformed into fuzzy domain after it is denoised.

Conclusion:

A cellular learning automata model is used to detect any abnormality on the image which is related to a lesion. The automaton is created with an extra term as the rule updating term to improve the adaptability and efficiency of the cellular automata.Three main statistical criteria are introduced as the sensitivity, specificity and accuracy. A number of 50 retinal images with visually detection hard exudates and lesions are the experimental dataset for evaluation and validation of the method.

Keywords: Digital retinal images, Hard exudates and lesions detection, Denoising, Fuzzification concept, Cellular Learning Automata, Statistical evaluation parameters, Ripplet transform.