Abstract
We propose an original scheme for the 3D segmentation of multi-echo MR brain images into white matter, gray matter and cerebro-spinal 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.
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Capelle, AS., 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: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_16
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DOI: https://doi.org/10.1007/978-3-540-39907-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20129-8
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