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Learning active shape models for bifurcating contours

Learning active shape models for bifurcating contours

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Original languageEnglish
Pages (from-to)666-677
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume26
Issue number5
DOIs
StatePublished - May 2007

Abstract

Statistical shape models are often learned from examples based on landmark correspondences between annotated examples. A method is proposed for learning such models from contours with inconsistent bifurcations and loops. Automatic segmentation of tibial and femoral contours in knee X-ray images is investigated as a step towards reliable, quantitative radiographic analysis of osteoarthritis for diagnosis and assessment of progression. Results are presented using various features, the Mahalanobis distance, distance weighted K-nearest neighbours, and two relevance vector machine-based methods as quality of fit measure

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