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Proceedings of International Conference on Computer Vision and Image Processing pp 249–259Cite as

ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 459))

Abstract

A novel region of interest (ROI) segmentation for detection of Glioblastoma multiforme (GBM) tumor in magnetic resonance (MR) images of the brain is proposed using a two-stage thresholding method. We have defined multiple intervals for multilevel thresholding using a novel meta-heuristic optimization technique called Discrete Curve Evolution. In each of these intervals, a threshold is selected by bi-level Otsu’s method. Then the ROI is extracted from only a single seed initialization, on the ROI, by the user. The proposed segmentation technique is more accurate as compared to the existing methods. Also the time complexity of our method is very low. The experimental evaluation is provided on contrast-enhanced T1-weighted MRI slices of three patients, having the corresponding ground truth of the tumor regions. The performance measure, based on Jaccard and Dice indices, of the segmented ROI demonstrated higher accuracy than existing methods.

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Notes

  1. 1.

    “Brain tumor image data used in this work were obtained from the MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation (http://www.imm.dtu.dk/projects/BRATS2012) organized by B. Menze, A. Jakab, S. Bauer, M. Reyes, M. Prastawa, and K. Van Leemput. The challenge database contains fully anonymized images from the following institutions: ETH Zurich, University of Bern, University of Debrecen, and University of Utah.

    Note: the images in this database have been skull stripped”.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B. T Road, Kolkata, 700108, India

    Subhashis Banerjee, Sushmita Mitra & B. Uma Shankar

Authors
  1. Subhashis Banerjee
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  2. Sushmita Mitra
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  3. B. Uma Shankar
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Corresponding author

Correspondence to Subhashis Banerjee .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Partha Pratim Roy

  4. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Sen

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Banerjee, S., Mitra, S., Uma Shankar, B. (2017). ROI Segmentation from Brain MR Images with a Fast Multilevel Thresholding. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_23

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  • DOI: https://doi.org/10.1007/978-981-10-2104-6_23

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2103-9

  • Online ISBN: 978-981-10-2104-6

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