Segmentation based on information fusion applied to brain tissue on MRI

An information fusion based fuzzy segmentation method applied to Magnetic Resonance Images (MRI) is proposed in this paper. It can automatically extract the normal and abnormal tissues of human brain from multispectral images such as T1-weighted, T2-weighted and Proton Density (PD) feature images. Fuzzy models of normal tissues corresponding to three MRI sequences images are derived from histogram according to a priori knowledge. Three different functions are chosen to calculate the fuzzy models of abnormal tissues. Then, the fuzzy features extracted by these fuzzy models are joined by a fuzzy relation operator which represents their fuzzy feature fusion. The final segmentation result is obtained by a fuzzy region growing based fuzzy decision rule. The experimental results of the proposed method are compared with the manually labeled segmentation by a neuroradiologist for abnormal tissues and with anatomic model of BrainWeb for normal tissues. The MRI images used in our experiment are imaged with a 1.5T GE for abnormal brain, with 3D MRI simulated brain database for normal brain by using an axial 3D IR T1-weighted (TI/TR/TE: 600/10/2), an axial FSE T2-weighted(TR/TE: 3500/102) and an axial FSE PD weighted (TR/TE: 3500/11). Based on 4 patients studied, the average probability of false detection of abnormal tissues is 5%. For the normal tissues, a false detection rate of 4% 15% is obtained in images with 3% 7% noise level. All of them show a good performance for our method.