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Journal of Healthcare Engineering
Volume 1, Issue 1, Pages 1-11
Research Article

Detection of Pathological Myopia by PAMELA with Texture-Based Features through an SVM Approach

Jiang Liu,1 Damon W. K. Wong,1 Joo Hwee Lim,1 Ngan Meng Tan,1 Zhuo Zhang,1 Huiqi Li,1 Fengshou Yin,1 Benghai Lee,1 Seang Mei Saw,2 Louis Tong,3 and Tien Yin Wong3

1Institute for Infocomm Research, A*STAR, Singapore
2National University of Singapore, Singapore
3Singapore Eye Research Institute, Singapore

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Pathological myopia is the seventh leading cause of blindness worldwide. Current methods for the detection of pathological myopia are manual and subjective. We have developed a system known as PAMELA (Pathological Myopia Detection Through Peripapillary Atrophy) to automatically assess a retinal fundus image for pathological myopia. This paper focuses on the texture analysis component of PAMELA which uses texture features, clinical image context and support vector machine-based classification to detect the presence of pathological myopia in a retinal fundus image. Results on a test image set from the Singapore Eye Research Institute show an accuracy of 87.5% and a sensitivity and specificity of 0.85 and 0.90 respectively. The results show good promise for PAMELA to be developed as an automatic tool for pathological myopia detection.