City Research Online

Abstract

Imaging and evaluation of the optic nerve head (ONH) plays an essential part in the detection and clinical management of glaucoma. The morphological characteristics of ONHs vary greatly from person to person and this variability means it is difficult to quantify them in a standardized way. We developed and evaluated a feature extraction approach using shiftinvariant wavelet packet and kernel principal component analysis to quantify the shape features in ONH images acquired by scanning laser ophthalmoscopy (Heidelberg Retina Tomograph [HRT]). The methods were developed and tested on 1996 eyes from three different clinical centers. A shape abnormality score (SAS) was developed from extracted features using a Gaussian process to identify glaucomatous abnormality. SAS can be used as a diagnostic index to quantify the overall likelihood of ONH abnormality. Maps showing areas of likely abnormality within the ONH were also derived. Diagnostic performance of the technique, as estimated by ROC analysis, was significantly better than the classification tools currently used in the HRT software – the technique offers the additional advantage of working with all images and is fully automated.

Publication Type:	Article
Additional Information:	This paper was published in Biomedical Optics Express and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://dx.doi.org/10.1364/BOE.5.002215. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.
Publisher Keywords:	Image analysis, Pattern recognition, Baysian processors, Wavelets, Defect understanding, Ophthalmology, Retina scanning, Optical diagnostics for medicine
Subjects:	R Medicine > RE Ophthalmology
Departments:	School of Health & Psychological Sciences > Optometry & Visual Sciences
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

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