Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

Madeleine León; Boris Escalante-Ramirez

doi:10.1117/12.2035534

19 November 2013 Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images

Madeleine León, Boris Escalante-Ramirez

Author Affiliations +

Proceedings Volume 8922, IX International Seminar on Medical Information Processing and Analysis; 892214 (2013) https://doi.org/10.1117/12.2035534
Event: IX International Seminar on Medical Information Processing and Analysis, 2013, Mexico City, Mexico

Abstract

Knee osteoarthritis (OA) is characterized by the morphological degeneration of cartilage. Efficient segmentation of cartilage is important for cartilage damage diagnosis and to support therapeutic responses. We present a method for knee cartilage segmentation in magnetic resonance images (MRI). Our method incorporates the Hermite Transform to obtain a hierarchical decomposition of contours which describe knee cartilage shapes. Then, we compute a statistical model of the contour of interest from a set of training images. Thereby, our Hierarchical Active Shape Model (HASM) captures a large range of shape variability even from a small group of training samples, improving segmentation accuracy. The method was trained with a training set of 16- MRI of knee and tested with leave-one-out method.

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Madeleine León and Boris Escalante-Ramirez "Segmentation of knee cartilage by using a hierarchical active shape model based on multi-resolution transforms in magnetic resonance images", Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 892214 (19 November 2013); https://doi.org/10.1117/12.2035534

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