Automatic detection of liver tumors

Abstract : Tumor detection in CT liver images is a challenging task. The nature of tumor has a direct effect on the number of voxels being contaminated, as well as on the changes in the observed CT scan. In order to deal with this challenge, in this paper we propose the use of advanced non-linear machine learning techniques to determine the optimal features, as well as the hyperplane that use these features to separate tumoral voxels from voxels corresponding to healthy tissues. Very promising classification results using an important volume of clinically annotated data (86% sensitivity, 82% specificity) demonstrate the potentials of our approach.
Type de document :
Communication dans un congrès
ISBI, 2008, France. pp.672-675, 2008
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Contributeur : Enzo Ferrante <>
Soumis le : samedi 14 décembre 2013 - 12:35:31
Dernière modification le : mardi 5 février 2019 - 13:52:14


  • HAL Id : hal-00918720, version 1



Pescia Daniel, Nikos Paragios, Stéphane Chemouny. Automatic detection of liver tumors. ISBI, 2008, France. pp.672-675, 2008. 〈hal-00918720〉



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