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Automatic Region-Based Brain Classification of MRI-T1 Data

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  • Sepideh Yazdani
  • Rubiyah Yusof
  • Alireza Karimian
  • Yasue Mitsukira
  • Amirshahram Hematian

Abstract

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.

Suggested Citation

  • Sepideh Yazdani & Rubiyah Yusof & Alireza Karimian & Yasue Mitsukira & Amirshahram Hematian, 2016. "Automatic Region-Based Brain Classification of MRI-T1 Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0151326
    DOI: 10.1371/journal.pone.0151326
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    1. Marjan Mansourvar & Shahaboddin Shamshirband & Ram Gopal Raj & Roshan Gunalan & Iman Mazinani, 2015. "An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-14, September.
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