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A Scalable Framework For Segmenting Magnetic Resonance Images

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Abstract

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.

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Acknowledgements

This research was partially supported by the National Institutes of Health under grant number 1 R01 EB00822-01 and by the Department of Energy through the ASCI PPPE Data Discovery Program, Contract number: DE-AC04-76DO00789.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA

    Prodip Hore, Lawrence O. Hall, Dmitry B. Goldgof & Yuhua Gu

  2. School of Medicine, University of Miami, Coral Gables, FL, USA

    Andrew A. Maudsley & Ammar Darkazanli

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  1. Prodip Hore
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  2. Lawrence O. Hall
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  3. Dmitry B. Goldgof
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Corresponding author

Correspondence to Lawrence O. Hall.

Appendix

Appendix

Table 9 1.5 Tesla: Comparison of segmentations of SPFCM VS FSL.
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Table 10 1.5 Tesla: Comparison of segmentations of SPM2 VS FSL.
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Table 11 3 Tesla: Comparison of segmentations of SPFCM VS FSL.
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Table 12 3 Tesla: Comparison of segmentations of SPM2 VS FSL.
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Table 13 1.5 Tesla: Comparison of segmentations of OFCM VS FSL.
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Table 14 3 Tesla: Comparison of segmentations of OFCM VS FSL.
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Hore, P., Hall, L.O., Goldgof, D.B. et al. A Scalable Framework For Segmenting Magnetic Resonance Images. J Sign Process Syst Sign Image Video Technol 54, 183–203 (2009). https://doi.org/10.1007/s11265-008-0243-1

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  • DOI: https://doi.org/10.1007/s11265-008-0243-1

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