We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Corso, J. J., Sharon, E., & Yuille, A. (2006). Multilevel segmentation and integrated bayesian model classification with an application to brain tumor segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4191 LNCS-II, pp. 790–798). Springer Verlag. https://doi.org/10.1007/11866763_97
Mendeley helps you to discover research relevant for your work.