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. Author manuscript; available in PMC: 2011 Jul 7.
Published in final edited form as: IEEE Trans Med Imaging. 2010 Jul 19;29(12):2023–2037. doi: 10.1109/TMI.2010.2058861

Fig. 5.

Fig. 5

Summary of classifier-learning stages of LOGISMOS based segmentation of articular cartilage for all bones in the knee joint. A total of 12 classifiers was trained and utilized. Complete expert-defined VOIs are available for TRAIN-1 (25 MR images). Complete tracings of bone and cartilage surfaces are available for TRAIN-2 (9 MR images) dataset. Note that individual steps are separately validated. The TEST dataset is large and consists of 60 MR datasets, for which dense but not complete tracing of bone and cartilage surfaces is available. (a) Learning of VOI properties in TRAIN-1 dataset, Adaboost localization of individual bones VOIs, leave-one-out validation in TRAIN-1. (b) Learning of bone surface properties in TRAIN-2 dataset, leave-one-out validation in TRAIN-2. (c) Learning of cartilage/non-cartilage location properties in TRAIN-2 dataset, Adaboost classification of cartilage/non-cartilage regions, (also leave-one-out validation in TRAIN-2). (d) Learning cartilage regional properties in TRAIN-2 dataset, final validation in TEST dataset, (also leave-one-out validation in TRAIN-2).