Hierarchical detection of multiple organs using boosted features

Authors:
Samuel Hugueny;Mikaël Rousson
Affiliations:
Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ;Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJ
Venue:
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Year:
2007

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Abstract

We propose a framework for fast and automated initialization of segmentation algorithms in Computed Tomography images. Based on the idea that time-consuming voxel classification should be done only on spatially constrained areas, we build classifiers at body and slice levels which quickly define a constrained region of interest. Voxel classification is then performed by a divide-and-conquer strategy using a probabilistic-boosting tree. In addition, this framework can incorporate additional information on the volume, if available, such as the position of another organ to improve its accuracy and robustness. The framework is applied to seed extraction in kidneys and liver.