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 symmetrybased 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Donner, R., Micusik, B., Langs, G., Szumilas, L., Peloschek, P., Friedrich, K., & Bischof, H. (2007). Object localization based on markov random fields and symmetry interest points. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4792 LNCS, pp. 460–468). Springer Verlag. https://doi.org/10.1007/978-3-540-75759-7_56
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