Hierarchical Detection of Multiple Organs Using Boosted Features

  • Samuel Hugueny
  • Mikaël Rousson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)


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.


Seed Extraction Voxel Level Left Subtree Slice Level Computer Assisted Diagnosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Samuel Hugueny
    • 1
  • Mikaël Rousson
    • 1
  1. 1.Department of Imaging and Visualization, Siemens Corporate Research, Princeton, NJUSA

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