Knowledge Based and Statistical Based Approaches in Biomedical Image Analysis

  • Florence Cloppet
  • Thomas Hurtut
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 120)


Biomedical Imaging has grown significantly for the past twenty years, as it is considered as a unique method for visualizing biological processes within living organisms in a non-invasive manner. Although works in biomedical image analysis rely on underlying biological problems, scientists are just beginning to embrace the idea that these works will benefit from multidisciplinary interactions. Moreover, within the computer vision community, time has come for a more holistic and integrated approach in order to articulate statistical/machine learning and knowledge-based approaches. In this paper we present studies based on these two classic approaches and show how their complementarity may benefit biomedical imaging.


Active Contour Shape Descriptor Biomedical Image Gray Level Intensity Computer Vision Community 
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 Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de Paris Descartes (LIPADE)Paris Descartes UniversityParisFrance

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