Abstract
Expectation Maximization (EM) algorithm is an unsupervised clustering algorithm, but initialization information especially the number of clusters is crucial to its performance. In this paper, a new MRI segmentation method based on scale-space theory and EM algorithm has been proposed. Firstly, gray level density of a brain MRI is estimated; secondly, the corresponding fingerprints which include initialization information for EM using scale-space theory are obtained; lastly, segmentation results are achieved by the initialized EM. During the initialization phase, restrictions of clustering component weights decrease the influence of noise or singular points. Brain MRI segmentation results indicate that our method can determine more reliable initialization information and achieve more accurate segmented tissues than other initialization methods.
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Acknowledgement
The paper is supported by the following fund projects: The National Natural Science Foundation of China (61402204); The Natural Science Foundation of Jiangsu Province (BK20130529); Research Fund for Advanced Talents of Jiangsu University(14JDG141); Science and Technology Project of Zhenjiang City (SH20140110); China Postdoctoral Science Foundation (Project No. 2014M551324); Special Software Development Foundation of Zhenjiang City (No. 201322); Science and Technology Support Foundation of Zhenjiang City(Industrial) (GY2014013).
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Song, Y., Bao, X., Liu, Z., Yuan, D., Song, M. (2015). An Improved Brain MRI Segmentation Method Based on Scale-Space Theory and Expectation Maximization Algorithm. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_52
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DOI: https://doi.org/10.1007/978-3-319-24078-7_52
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