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
Identifiers and Pagination:Year: 2019
First Page: 8
Last Page: 17
Publisher Id: TOMIJ-11-8
Article History:Received Date: 17/06/2019
Revision Received Date: 07/11/2019
Acceptance Date: 11/11/2019
Electronic publication date: 20/12/2019
Collection year: 2019
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.
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.
An efficient segmentation method is then needed to detect and discern the lesions from the retinal area.
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.
Ripplet transform and cycle spinning method is first used to remove the noises and artifacts.
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.
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.