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.

Date Published: 19 November 2013
PDF: 8 pages
Proc. SPIE 8922, IX International Seminar on Medical Information Processing and Analysis, 892214 (19 November 2013); doi: 10.1117/12.2035534

Published in SPIE Proceedings Vol. 8922:
IX International Seminar on Medical Information Processing and Analysis
Jorge Brieva; Boris Escalante-Ramírez, Editor(s)