Automatic detection of liver tumors - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2008

Automatic detection of liver tumors

Résumé

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.
Fichier non déposé

Dates et versions

hal-00918720 , version 1 (14-12-2013)

Identifiants

Citer

Pescia Daniel, Nikos Paragios, Stéphane Chemouny. Automatic detection of liver tumors. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, May 2008, Paris, France. pp.672-675, ⟨10.1109/ISBI.2008.4541085⟩. ⟨hal-00918720⟩

Collections

EC-PARIS
146 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More