ACCURATE AND ROBUST SHAPE DESCRIPTORS FOR THE IDENTIFICATION OF RIB CAGE STRUCTURES IN CT-IMAGES WITH RANDOM FORESTS - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2013

ACCURATE AND ROBUST SHAPE DESCRIPTORS FOR THE IDENTIFICATION OF RIB CAGE STRUCTURES IN CT-IMAGES WITH RANDOM FORESTS

Résumé

This paper presents a new automatic technique for the segmentation of the rib cage on CT images. Motivated by a usage scenario in the context of large, heterogeneous databases of CT-images, we introduce two shape descriptors to be used in conjunction with a Random Forests (RF) classifier. These descriptors were specifically designed to address the challenges of rib identification under various acquisition conditions affecting subject’s orientation and image quality. Extensive experiments demonstrate the superiority of our proposed shape descriptors in nominal configurations. Robustness with respect to subject’s orientation variation and additive noise is also demonstrated, with an improvement of classification performance of up to 25%, comparing to intensity-based descriptors, without neither pre-registration nor pre-smoothing.
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Dates et versions

hal-01206863 , version 1 (01-10-2015)

Identifiants

  • HAL Id : hal-01206863 , version 1

Citer

Mariem Gargouri, Julien Tierny, Erwan Jolivet, Philippe Petit, Elsa Angelini. ACCURATE AND ROBUST SHAPE DESCRIPTORS FOR THE IDENTIFICATION OF RIB CAGE STRUCTURES IN CT-IMAGES WITH RANDOM FORESTS . IEEE International Symposium on BIOMEDICAL IMAGING, Apr 2013, San Francisco, United States. ⟨hal-01206863⟩
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