Graph-based Detection, Segmentation & Characterization of Brain Tumors - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2012

Graph-based Detection, Segmentation & Characterization of Brain Tumors

Abstract

In this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with image based classification techniques along with spatial smoothness constraints towards producing a reliable detection map within a unified graphical model formulation. Towards optimal use of prior knowledge, a two layer interconnected graph is considered with one layer corresponding to the low-grade glioma type (characterization) and the second layer to voxel-based decisions of tumor presence. Efficient linear programming both in terms of performance as well as in terms of computational load is considered to recover the lowest potential of the objective function. The outcome of the method refers to both tumor segmentation as well as their characterization. Promising results on substantial data sets demonstrate the extreme potentials of our method.

Domains

Medical Imaging
Fichier principal
Vignette du fichier
CVPR_1620.pdf (1.25 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00712714 , version 1 (27-06-2012)

Identifiers

  • HAL Id : hal-00712714 , version 1

Cite

Sarah Parisot, Hugues Duffau, Stéphane Chemouny, Nikos Paragios. Graph-based Detection, Segmentation & Characterization of Brain Tumors. CVPR - 25th IEEE Conference on Computer Vision and Pattern Recognition 2012, Jun 2012, Providence, United States. ⟨hal-00712714⟩
400 View
562 Download

Share

Gmail Facebook Twitter LinkedIn More