Abstract
A novel hybrid algorithm for the tissue segmentation of brain magnetic resonance images is proposed. The core of the algorithm is a probabilistic neural network (PNN) in which weighting factors are added to the summation layer, such that partial volume effects can be taken into account in the modeling process. The mean vectors for the probability density function estimation and the corresponding weighting factors are generated by a hierarchical scheme involving a self-organizing map neural network and an expectation maximization algorithm. Unlike conventional PNN, this approach circumvents the need for training sets. Tissue segmentation results from various algorithms are compared and the effectiveness and robustness of the proposed approach are demonstrated.
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Acknowledgements
The authors would like to thank the Autonomous Control Engineering Center at the University of New Mexico and the following people for their helpful contributions: Dr. Lee Friedman of The MIND Institute and Dr. Vincent Magnotta of the University of Iowa. This work was supported in part by a Merit Review grant from the Department of Veteran Affairs and the MIND Institute (Albuquerque).
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Song, T., Gasparovic, C., Andreasen, N. et al. A hybrid tissue segmentation approach for brain MR images. Med Bio Eng Comput 44, 242–249 (2006). https://doi.org/10.1007/s11517-005-0021-1
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DOI: https://doi.org/10.1007/s11517-005-0021-1