A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images

Authors:
Weibei Dou;Su Ruan;Yanping Chen;Daniel Bloyet;Jean-Marc Constans
Affiliations:
Department of Electronic Engineering, Tsinghua University, 100084 Beijing, China and GREYC-CNRS UMR 6072, 6 Boulevard Maréchal Juin, 14050 Caen, France;cCReSTIC, 9 Rue de Qubec, 10026 Troyes, France;Imaging Diagnostic Center, Nanfang Hospital, Guangzhou, China;GREYC-CNRS UMR 6072, 6 Boulevard Maréchal Juin, 14050 Caen, France;Unité d'IRM, EA3916, CHRU, 14033 Caen, France
Venue:
Image and Vision Computing
Year:
2007

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Abstract

A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a automatic and a precise segmentation. However, the terminology used by radiology experts are variable in term of image signal. In order to benefit of these descriptions, we propose to modellize them by fuzzy models. One fuzzy model is built for one type of MRI sequence. The segmentation is finally based on a fusion of different fuzzy information obtained from different types of MRI images. Our algorithm consists of four stages: the registration of multispectral MR images, the creation of fuzzy models describing the characteristics of tumor, the fusion based on fuzzy fusion operators and the adjustment by fuzzy region growing based on fuzzy connecting. The comparison between the obtained results and the hand-tracings of a radiology expert shows that the proposed algorithm is efficient. An average probability of correct detection 96% and an average probability of false detection 5% are obtained through studies of four patients.