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.
Highlights
IntroductionDue to its prevalence and clinical significance, the research community has attempted to improve its diagnosis and treatment by developing algorithms to perform retinal image analysis [4], fundus image enhancement [5], and monitoring [6]
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
Summary
Due to its prevalence and clinical significance, the research community has attempted to improve its diagnosis and treatment by developing algorithms to perform retinal image analysis [4], fundus image enhancement [5], and monitoring [6]. Of special significance are automatic image analysis algorithms designed to detect Hard Exudates (HEs) [7]. HEs have been found to be the most specific markers for the presence of retinal oedema, the major cause of visual loss in non-proliferative forms of DR [2]. Image denoising is a tool by which a good estimate of the original image from noisy states is provided. An efficient segmentation method is needed to detect and discern the lesions from the retinal area
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