Abstract
We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov Random Fields, and the detection is performed in a single iteration by the max-sum algorithm. Instead of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. The image information is captured by symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.
This research has been supported by the Austrian Science Fund (FWF) under grants P17083-N04 (AAMIR) and P17189-N04 (SESAME), as well as the European Union Network of Excellence FP6-507752 (MUSCLE) and the Region Île-de-France.
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Donner, R. et al. (2007). Object Localization Based on Markov Random Fields and Symmetry Interest Points. In: Ayache, N., Ourselin, S., Maeder, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. MICCAI 2007. Lecture Notes in Computer Science, vol 4792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75759-7_56
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DOI: https://doi.org/10.1007/978-3-540-75759-7_56
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