Detection of pathological myopia by PAMELA with texture-based features through an SVM approach

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

doi:10.1260/2040-2295.1.1.1

Detection of pathological myopia by PAMELA with texture-based features through an SVM approach

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

Research output: Journal Publication › Article › peer-review

40 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1-11
Number of pages	11
Journal	Journal of Healthcare Engineering
Volume	1
Issue number	1
DOIs	https://doi.org/10.1260/2040-2295.1.1.1
Publication status	Published - Mar 2010
Externally published	Yes

Keywords

Computer aided detection
Pathological myopia
Peripapillary atrophy

ASJC Scopus subject areas

Biotechnology
Surgery
Biomedical Engineering
Health Informatics

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10.1260/2040-2295.1.1.1

Cite this

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title = "Detection of pathological myopia by PAMELA with texture-based features through an SVM approach",

abstract = "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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year = "2010",

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AU - Liu, Jiang

AU - Wong, Damon W.K.

AU - Lim, Joo Hwee

AU - Tan, Ngan Meng

AU - Zhang, Zhuo

AU - Li, Huiqi

AU - Yin, Fengshou

AU - Lee, Benghai

AU - Saw, Seang Mei

AU - Tong, Louis

AU - Wong, Tien Yin

PY - 2010/3

Y1 - 2010/3

N2 - 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.

AB - 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.

KW - Computer aided detection

KW - Pathological myopia

KW - Peripapillary atrophy

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Detection of pathological myopia by PAMELA with texture-based features through an SVM approach

Abstract

Keywords

ASJC Scopus subject areas

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