SVM-based texture classification in optical coherence tomography

N Anantrasirichai; Alin M Achim; James Morgan; Irina Erchova; Lindsay B Nicholson

doi:10.1109/ISBI.2013.6556778

SVM-based texture classification in optical coherence tomography

N Anantrasirichai, Alin M Achim, James Morgan, Irina Erchova, Lindsay B Nicholson

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

46 Citations (Scopus)

436 Downloads (Pure)

Abstract

This paper describes a new method for automated texture
classification for glaucoma detection using high resolution
retinal Optical Coherence Tomography (OCT). OCT is a
non-invasive technique that produces cross-sectional imagery
of ocular tissue. Here, we exploit information from OCT images,
specifically the inner retinal layer thickness and speckle
patterns, to detect glaucoma. The proposed method relies on
support vector machines (SVM), while principal component
analysis (PCA) is also employed to improve classification
performance. Results show that texture features can improve
classification accuracy over what is achieved using only layer
thickness as existing methods currently do.

Original language	English
Title of host publication	IEEE International Symposium on Biomedical Imaging
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2013.6556778
Publication status	Published - 2013

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10.1109/ISBI.2013.6556778

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Accepted author manuscript, 1.38 MB

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Cite this

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title = "SVM-based texture classification in optical coherence tomography",

abstract = "This paper describes a new method for automated textureclassification for glaucoma detection using high resolutionretinal Optical Coherence Tomography (OCT). OCT is anon-invasive technique that produces cross-sectional imageryof ocular tissue. Here, we exploit information from OCT images,specifically the inner retinal layer thickness and specklepatterns, to detect glaucoma. The proposed method relies onsupport vector machines (SVM), while principal componentanalysis (PCA) is also employed to improve classificationperformance. Results show that texture features can improveclassification accuracy over what is achieved using only layerthickness as existing methods currently do.",

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AB - This paper describes a new method for automated textureclassification for glaucoma detection using high resolutionretinal Optical Coherence Tomography (OCT). OCT is anon-invasive technique that produces cross-sectional imageryof ocular tissue. Here, we exploit information from OCT images,specifically the inner retinal layer thickness and specklepatterns, to detect glaucoma. The proposed method relies onsupport vector machines (SVM), while principal componentanalysis (PCA) is also employed to improve classificationperformance. Results show that texture features can improveclassification accuracy over what is achieved using only layerthickness as existing methods currently do.

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