Handayani Tjandrasa

Work place: Department of Informatics Sepuluh Nopember Institute of Technology (ITS) Surabaya, Indonesia

E-mail: handatj@its.ac.id


Research Interests: Engineering, Image Processing, Image Manipulation, Image Compression, Computational Engineering


Handayani Tjandrasa received her Ph.D. degree in Electrical and Computer Engineering from University of Wisconsin, Madison, USA, in 1984. She is currently a professor at the Department of Informatics, Sepuluh Nopember Institute of Technology (ITS), Surabaya, Indonesia. Her research interests are in the areas of digital image processing, biomedical engineering, and pattern classification

Author Articles
Classification of Non-Proliferative Diabetic Retinopathy Based on Segmented Exudates using K-Means Clustering

By Handayani Tjandrasa Isye Arieshanti Radityo Anggoro

DOI: https://doi.org/10.5815/ijigsp.2015.01.01, Pub. Date: 8 Dec. 2014

Diabetic retinopathy is a severe complication retinal disease caused by advanced diabetes mellitus. Long suffering of this disease without threatment may cause blindness. Therefore, early detection of diabetic retinopathy is very important to prevent to become proliferative. One indication that a patient has diabetic retinopathy is the existence of hard exudates besides other indications such as microaneurysms and hemorrhages. In this study, the existence of hard exudates is applied to classify the moderate and severe grading of non-proliferative diabetic retinopathy in retinal fundus images. The hard exudates are segmented using K-means clustering. The segmented regions are extracted to obtain a feature vector which consists of the areas, the perimeters, the number of centroids and its standard deviation. Using three different classifiers, i.e. soft margin Support Vector Machine, Multilayer Perceptron, and Radial Basis Function Network, we achieve the accuracy of 89.29%, 91.07%, and 85.71% respectively, for 56 training data and 56 testing data of retinal images.

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