Abstract
To overcome the limitations of standard expectation maximization (EM) algorithm, an improved EM algorithm is proposed. Based on this algorithm, a novel statistical approach for segmentation of brain magnetic resonance (MR) image data is presented in this paper, which involves three steps. Firstly, after pre-processing the image with the curvature anisotropic diffusion filter, the background (BG) and brain masks of the image are obtained by applying a combination approach of thresholding with morphology. Secondly, the connected threshold region growing technique is employed to get the preliminary results of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) on a brain MRI. Finally, the previous results are served as the priori knowledge for the improved EM algorithm to segment the brain MRI. The performance of the proposed method is compared with those of standard EM algorithm and the popular used fuzzy-C means (FCM) segmentation. Experimental results show our approach is effective, robust and significantly faster than the conventional EM based method.
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Yang, Y. (2006). An Improved EM Algorithm for Statistical Segmentation of Brain MRI. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_8
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DOI: https://doi.org/10.1007/11875581_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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