We propose an original scheme for the 3D segmentation of multi-echo MR brain images into white matter, gray matter and cerebrospinal fluid. To take into account complementary, redundancy and eventual conflicts provided by the different echoes, a fusion process based on Evidence theory is used. Such theory, well suited to imprecise and uncertain data, provides great fusion tools. The originality of our method is to include a regularization process by the mean of Dempster's combination. Adding neighborhood information increases the knowledge. The segmentation is more confident, accurate and efficient. The method is applied to simulated multi-echo data and compared with method based on Markov Random Field theory. The results are very encouraging and show that Evidence theory is well suited to such problematic. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Capelle, A. S., Colot, O., & Fernandez-Maloigne, C. (2003). 3D segmentation of MR brain images into white matter, gray matter and cerebro-spinal fluid by means of evidence theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2780 LNAI, pp. 112–116). https://doi.org/10.1007/978-3-540-39907-0_16
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