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Abstract #70304 Published in IGR 18-2
Atlas-based shape analysis and classification of retinal optical coherence tomography images using the functional shape (fshape) framework
Lee S; Charon N; Charlier B; Popuri K; Lebed E; Sarunic MV; Trouvé A; Beg MFMedical Image Analysis 2017; 35: 570-581
We propose a novel approach for quantitative shape variability analysis in retinal optical coherence tomography images using the functional shape (fshape) framework. The fshape framework uses surface geometry together with functional measures, such as retinal layer thickness defined on the layer surface, for registration across anatomical shapes. This is used to generate a population mean template of the geometry-function measures from each individual. Shape variability across multiple retinas can be measured by the geometrical deformation and functional residual between the template and each of the observations. To demonstrate the clinical relevance and application of the framework, we generated atlases of the inner layer surface and layer thickness of the Retinal Nerve Fiber Layer (RNFL) of glaucomatous and normal subjects, visualizing detailed spatial pattern of RNFL loss in glaucoma. Additionally, a regularized linear discriminant analysis classifier was used to automatically classify glaucoma, glaucoma-suspect, and control cases based on RNFL fshape metrics.
School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, V5A 1S6, Canada.
Full articleClassification:
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
