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.
Magnetic resonance image analysis by computer is useful to aid diagnosis of malady. We present in this paper a automatic segmentation method for principal brain tissues. It is based on the possibilistic clustering approach, which is an improved fuzzy c-means clustering method. In order to improve the efficiency of clustering process, the initial value problem is discussed and solved by combining with a histogram analysis method. Our method can automatically determine number of classes to cluster and the initial values for each class. It has been tested on a set of forty MR brain
images with or without the presence of tumor. The experimental results showed that it is simple, rapid and robust to segment the principal brain tissues.
This paper presents a fuzzy information fusion method to automatically extract tumor areas of human brain from multispectral magnetic resonance (MR) images. The multispectral images consist of T1 -weighted (T1), proton density (PD), and 12-weighted (T2) feature images, in which signal intensities of a tumor are different. Some tissue is more visible in one image type than the others. The fusion of information is therefore necessary. Our method, based on the fusion of information, model the fuzzy information about the tumor by membership functions. Thismodelisation is based on the a priori knowledge of radiology experts and the MR signals of the brain tissues. Three membership functions related to the three images types are proposed according to their characteristics. The brain extraction is then carried out by using the fusion of all three fuzzy information. The experimental results (based on 5 patients studied) show a mean false-negative of 2% and a mean false-positive of 1 .3%, comparing to the results obtained by a radiology using manual tracing.
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