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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Statistical Shape Modeling Using MDL Incorporating Shape, Appearance, and Expert Knowledge
verfasst von : Aaron D. Ward, Ghassan Hamarneh
Erschienen in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
Verlag: Springer Berlin Heidelberg
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We propose a highly automated approach to the point correspondence problem for anatomical shapes in medical images. Manual landmarking is performed on a
small subset
of the shapes in the study, and a machine learning approach is used to elucidate the characteristic
shape and appearance
features at each landmark. A classifier trained using these features defines a cost function that drives key landmarks to anatomically meaningful locations after MDL-based correspondence establishment. Results are shown for artificial examples as well as real data.