Journal of Healthcare Engineering

Journal of Healthcare Engineering / 2010 / Article

Research Article | Open Access

Volume 1 |Article ID 657574 |

Jiang Liu, Damon W. K. Wong, Joo Hwee Lim, Ngan Meng Tan, Zhuo Zhang, Huiqi Li, Fengshou Yin, Benghai Lee, Seang Mei Saw, Louis Tong, Tien Yin Wong, "Detection of Pathological Myopia by PAMELA with Texture-Based Features through an SVM Approach", Journal of Healthcare Engineering, vol. 1, Article ID 657574, 12 pages, 2010.

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


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.


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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.

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