Segmentation of multi-modality MR images by means of evidence theory for 3D reconstruction of brain tumors

Author

Capelle, A.-S. ; Colot, O. ; Fernandez-Maloigne, C.

Author_Institution

Lab. IRCOM-SIC, CNRS, France

Volume

2

fYear

2002

fDate

2002

Abstract

In this paper, we propose a segmentation scheme for magnetic resonance (MR) images based on a two step algorithm. The first step consists of a classification based on an evidential k-NN rule initially proposed by Denoeux (1995). The second step allows to take into account the spatial dependence of each voxel of the MR volume in order to lead the segmentation. The goal is to locate properly tumors in MR images of the brain allowing the 3D reconstruction of the different brain structures and the tumor. It can help clinicians observe the tumors accurately and to follow the evolution of the tumors in multidate acquisitions of MR images.

Keywords

biomedical MRI; brain; case-based reasoning; image classification; image reconstruction; image segmentation; medical image processing; spatial filters; tumours; 3D reconstruction; MR images; brain tumors; evidence theory; evidential classification algorithm; evidential k-NN rule; evidential spatial filtering; image segmentation; k-nearest neighbor rule; magnetic resonance images; multi-modality images; spatial dependence; two step algorithm; voxel; Adaptive algorithm; Brain; Filtering; Image reconstruction; Image segmentation; Neoplasms; Pattern recognition; Prototypes; Resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing. 2002. Proceedings. 2002 International Conference on

ISSN

1522-4880

Print_ISBN

0-7803-7622-6

Type

conf

DOI

10.1109/ICIP.2002.1040065

Filename

1040065