An investigation into neural networks for the detection of exudates in retinal images

Gerald Schaefer; Edmond Leung

doi:10.1007/978-3-540-88079-0_17

An investigation into neural networks for the detection of exudates in retinal images

Gerald Schaefer^*, Edmond Leung

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

We present an approach of automatically detecting exudates in retinal images using neural networks. Exudates are one of the early indicators of diabetic retinopathy which is known as one of the leading causes for blindness. A neural network is trained to classify whether small image windows are part of exudate areas or not. Furthermore, it is shown that a pre-processing step based on histogram specification in order to deal with varying lighting conditions greatly improves the recognition performance. Application of principal component analysis is used for dimensionality reduction and speed-up of the system. Experimental results were obtained on an image data set with known exudate locations and showed good classification performance with a sensitivity of 94.78% and a specificity of 94.29%.

Original language	English
Title of host publication	Applications of soft computing: updating the state of art
Editors	Erel Avineri, Mario Köppen, et al
Place of Publication	Berlin (DE)
Publisher	Springer
Pages	169-177
Number of pages	9
ISBN (Electronic)	978-3-540-88079-0
ISBN (Print)	978-3-540-88078-3
DOIs	https://doi.org/10.1007/978-3-540-88079-0_17
Publication status	Published - 11 Feb 2009
Event	12th Online World Conference on Soft Computing in Industrial Applications - Online Duration: 16 Oct 2007 → 26 Oct 2007

Publication series

Name	Advances in Soft Computing
Publisher	Springer
Volume	52
ISSN (Print)	1615-3871
ISSN (Electronic)	1860-0794

Conference

Conference	12th Online World Conference on Soft Computing in Industrial Applications
Abbreviated title	WSC12
Period	16/10/07 → 26/10/07

Keywords

diabetic retinopathy
exudate detection
histogram equalisation/specification
neural network
principal component analysis

Access to Document

10.1007/978-3-540-88079-0_17

Cite this

@inproceedings{293c0ae5310d4389940b6a82a8268281,

title = "An investigation into neural networks for the detection of exudates in retinal images",

abstract = "We present an approach of automatically detecting exudates in retinal images using neural networks. Exudates are one of the early indicators of diabetic retinopathy which is known as one of the leading causes for blindness. A neural network is trained to classify whether small image windows are part of exudate areas or not. Furthermore, it is shown that a pre-processing step based on histogram specification in order to deal with varying lighting conditions greatly improves the recognition performance. Application of principal component analysis is used for dimensionality reduction and speed-up of the system. Experimental results were obtained on an image data set with known exudate locations and showed good classification performance with a sensitivity of 94.78% and a specificity of 94.29%.",

keywords = "diabetic retinopathy, exudate detection, histogram equalisation/specification, neural network, principal component analysis",

author = "Gerald Schaefer and Edmond Leung",

year = "2009",

month = feb,

day = "11",

doi = "10.1007/978-3-540-88079-0_17",

language = "English",

isbn = "978-3-540-88078-3",

series = "Advances in Soft Computing",

publisher = "Springer",

pages = "169--177",

editor = "Erel Avineri and Mario K{\"o}ppen and {et al}",

booktitle = "Applications of soft computing: updating the state of art",

address = "Germany",

note = "12th Online World Conference on Soft Computing in Industrial Applications, WSC12 ; Conference date: 16-10-2007 Through 26-10-2007",

}

Schaefer, G & Leung, E 2009, An investigation into neural networks for the detection of exudates in retinal images. in E Avineri, M Köppen & et al (eds), Applications of soft computing: updating the state of art. Advances in Soft Computing, vol. 52, Springer, Berlin (DE), pp. 169-177, 12th Online World Conference on Soft Computing in Industrial Applications, 16/10/07. https://doi.org/10.1007/978-3-540-88079-0_17

An investigation into neural networks for the detection of exudates in retinal images. / Schaefer, Gerald; Leung, Edmond.
Applications of soft computing: updating the state of art. ed. / Erel Avineri; Mario Köppen; et al. Berlin (DE): Springer, 2009. p. 169-177 (Advances in Soft Computing; Vol. 52).

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - An investigation into neural networks for the detection of exudates in retinal images

AU - Schaefer, Gerald

AU - Leung, Edmond

PY - 2009/2/11

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N2 - We present an approach of automatically detecting exudates in retinal images using neural networks. Exudates are one of the early indicators of diabetic retinopathy which is known as one of the leading causes for blindness. A neural network is trained to classify whether small image windows are part of exudate areas or not. Furthermore, it is shown that a pre-processing step based on histogram specification in order to deal with varying lighting conditions greatly improves the recognition performance. Application of principal component analysis is used for dimensionality reduction and speed-up of the system. Experimental results were obtained on an image data set with known exudate locations and showed good classification performance with a sensitivity of 94.78% and a specificity of 94.29%.

AB - We present an approach of automatically detecting exudates in retinal images using neural networks. Exudates are one of the early indicators of diabetic retinopathy which is known as one of the leading causes for blindness. A neural network is trained to classify whether small image windows are part of exudate areas or not. Furthermore, it is shown that a pre-processing step based on histogram specification in order to deal with varying lighting conditions greatly improves the recognition performance. Application of principal component analysis is used for dimensionality reduction and speed-up of the system. Experimental results were obtained on an image data set with known exudate locations and showed good classification performance with a sensitivity of 94.78% and a specificity of 94.29%.

KW - diabetic retinopathy

KW - exudate detection

KW - histogram equalisation/specification

KW - neural network

KW - principal component analysis

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BT - Applications of soft computing: updating the state of art

A2 - Avineri, Erel

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T2 - 12th Online World Conference on Soft Computing in Industrial Applications

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ER -

An investigation into neural networks for the detection of exudates in retinal images

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Keywords

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