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

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

44 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

Access to Document

10.1260/2040-2295.1.1.1

Cite this

@article{690032f6a5ec4c109f5d40d7f948c397,

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.",

keywords = "Computer aided detection, Pathological myopia, Peripapillary atrophy",

author = "Jiang Liu and Wong, {Damon W.K.} and Lim, {Joo Hwee} and Tan, {Ngan Meng} and Zhuo Zhang and Huiqi Li and Fengshou Yin and Benghai Lee and Saw, {Seang Mei} and Louis Tong and Wong, {Tien Yin}",

year = "2010",

month = mar,

doi = "10.1260/2040-2295.1.1.1",

language = "English",

volume = "1",

pages = "1--11",

journal = "Journal of Healthcare Engineering",

issn = "2040-2295",

publisher = "John Wiley and Sons Ltd",

number = "1",

}

TY - JOUR

T1 - Detection of pathological myopia by PAMELA with texture-based features through an SVM approach

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

UR - http://www.scopus.com/inward/record.url?scp=84864064289&partnerID=8YFLogxK

U2 - 10.1260/2040-2295.1.1.1

DO - 10.1260/2040-2295.1.1.1

M3 - Article

AN - SCOPUS:84864064289

SN - 2040-2295

VL - 1

SP - 1

EP - 11

JO - Journal of Healthcare Engineering

JF - Journal of Healthcare Engineering

IS - 1

ER -

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this