Robust and Accurate Appearance Models Based on Joint Dictionary Learning Data from the Osteoarthritis Initiative

  • Deformable model-based approaches to 3D image segmentation have been shown to be highly successful. Such methodology requires an appearance model that drives the deformation of a geometric model to the image data. Appearance models are usually either created heuristically or through supervised learning. Heuristic methods have been shown to work effectively in many applications but are hard to transfer from one application (imaging modality/anatomical structure) to another. On the contrary, supervised learning approaches can learn patterns from a collection of annotated training data. In this work, we show that the supervised joint dictionary learning technique is capable of overcoming the traditional drawbacks of the heuristic approaches. Our evaluation based on two different applications (liver/CT and knee/MR) reveals that our approach generates appearance models, which can be used effectively and efficiently in a deformable model-based segmentation framework.

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Author:Anirban Mukhopadhyay, Oscar Morillo, Stefan Zachow, Hans Lamecker
Document Type:Article
Parent Title (English):Lecture Notes in Computer Science, Patch-Based Techniques in Medical Imaging. Patch-MI 2016
Volume:9993
First Page:25
Last Page:33
Year of first publication:2016
DOI:http://dx.doi.org/10.1007/978-3-319-47118-1_4

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