Abstract
This paper presents an integrated method for adaptive segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. The method intends to do the volume segmentation in a slice-by-slice manner. Firstly, some slices in the volume are segmented using an automatic algorithm composed of watershed, fuzzy clustering (Fuzzy C-Means) and re-segmentation. Then their adjacent slices can be segmented conveniently by propagating the information of them. The information is consisted of watershed lines and thresholds obtained from the re-segmentation approach. This integrated approach yields a robust and precise segmentation. The efficacy of the proposed algorithm is validated using extensive experiments.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, J., Kong, J., Lu, Y., Zhang, J., Zhang, B. (2006). Segmentation of 3-D MRI Brain Images Using Information Propagation. In: Yang, GZ., Jiang, T., Shen, D., Gu, L., Yang, J. (eds) Medical Imaging and Augmented Reality. MIAR 2006. Lecture Notes in Computer Science, vol 4091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11812715_44
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DOI: https://doi.org/10.1007/11812715_44
Publisher Name: Springer, Berlin, Heidelberg
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