Article: Early detection and classification of microaneurysms in retinal fundus images using sequential learning methods Journal: International Journal of Biomedical Engineering and Technology (IJBET) 2014 Vol.15 No.2 pp.128 - 143 Abstract: Diabetic retinopathy is the most common diabetic eye disease and causes blindness if not treated on time. Microaneurysms are one of the first clinical signs of diabetic retinopathy and appear as small red dots on fundus images. The incidence of blindness can be reduced by detecting microaneurysms at an earlier stage. In this paper, a new method is proposed for detection of microaneurysms from the colour fundus retinal images to assist the eye care specialist to examine large populations of patient. The microaneurysms are detected from the colour fundus image by applying the pre-processing techniques in order to remove the optic disc and similar blood vessels using morphological operations. The pre-processed image was then used for feature extraction, and these features were used for the purpose of classification. The classifiers used are support vector machine, Meta-cognitive Neural Network (McNN) and Self-adaptive Resource Allocation Network (SRAN), and their performances are compared and presented. Inderscience Publishers - linking academia, business and industry through research

Title: Early detection and classification of microaneurysms in retinal fundus images using sequential learning methods

Authors: M. Ponni Bala; S. Vijayachitra

Addresses: Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode 638052, Tamil Nadu, India ' Department of Electronics and Instrumentation Engineering, Kongu Engineering College, Perundurai, Erode 638052, Tamil Nadu, India

Abstract: Diabetic retinopathy is the most common diabetic eye disease and causes blindness if not treated on time. Microaneurysms are one of the first clinical signs of diabetic retinopathy and appear as small red dots on fundus images. The incidence of blindness can be reduced by detecting microaneurysms at an earlier stage. In this paper, a new method is proposed for detection of microaneurysms from the colour fundus retinal images to assist the eye care specialist to examine large populations of patient. The microaneurysms are detected from the colour fundus image by applying the pre-processing techniques in order to remove the optic disc and similar blood vessels using morphological operations. The pre-processed image was then used for feature extraction, and these features were used for the purpose of classification. The classifiers used are support vector machine, Meta-cognitive Neural Network (McNN) and Self-adaptive Resource Allocation Network (SRAN), and their performances are compared and presented.

Keywords: diabetic retinopathy; microaneurysms; SVM; support vector machines; McNNs; metacognitive neural networks; SRAN; self-adaptive resource allocation networks; early detection; aneurysm classification; retinal fundus images; sequential learning; diabetic eye diseases; pre-processing; feature extraction; retinal images.

DOI: 10.1504/IJBET.2014.062743

International Journal of Biomedical Engineering and Technology, 2014 Vol.15 No.2, pp.128 - 143

Received: 17 Feb 2014
Accepted: 11 May 2014
Published online: 21 Oct 2014 *

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Title: Early detection and classification of microaneurysms in retinal fundus images using sequential learning methods

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