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 proceedingConference Contribution (Conference Proceeding)

46 Citations (Scopus)
436 Downloads (Pure)


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 languageEnglish
Title of host publicationIEEE International Symposium on Biomedical Imaging
Number of pages4
Publication statusPublished - 2013

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