Classification of Retinal Images Based on Statistical Moments and Principal Component Analysis
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Date
2014-09-23
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Early diagnosis of Diabetic Retinopathy (DR) has been suggested as a good measure of preventing
blindness associated with Diabetes. Some of the reported methodologies of Retinal Images (RI)
classification for early diagnosis of DR have been shown to involve several steps and approaches for
effective and accurate diagnosis. Thus, this paper investigates the classification of RI using a two-stage
procedure. The first stage includes the extraction of blood vessels from RI belonging to healthy and diabetes
retinal images using a modified local entropy thresholding algorithm. In the second stage, different features
are extracted including statistical moments and principal components. The set of extracted features is
combined into one feature vector and fed into a Sequential Minimal Optimization (SMO) classifier. The
obtained result is encouraging with an average accuracy of 68.33 %.
Description
Keywords
Principal component analysis, Biomedical imaging, Computers, Abstracts, Retina, Optimization, Graphics
Citation
Salami, M. J., Khorshidtalab, A., Baali, A., & Aibinu, A. M. (2014, September). Classification of Retinal Images Based on Statistical Moments and Principal Component Analysis. In 2014 International Conference on Computer and Communication Engineering (pp. 92-95). IEEE.