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Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature

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The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 407))

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Abstract

Deformation-based features has been proven effective for enhancing brain tumor segmentation accuracy. In our previous work, a component for extracting features based on brain lateral ventricular (LaV) deformation has been proposed. By employing the extracted feature on classifiers of artificial neural networks (ANN) and support vector machines (SVM), we have demonstrated its effect for enhancing brain magnetic resonance (MR) image tumor segmentation accuracy with supervised segmentation methods. In this paper, we propose an unsupervised brain tumor segmentation system with the use of extracted brain LaV deformation feature. By modifying the LaV deformation feature component, deformation-based feature is combined with MR image features as input dataset for the unsupervised fuzzy c-means (FCM) to perform clustering. Experimental results shows the positive effect from the deformation-based feature on FCM-based unsupervised brain tumor segmentation accuracy.

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Notes

  1. 1.

    The healthy brain image data used in this work were obtained from the Whole Brain Atlas (http://www.med.havardedu/aanlib), by K. A. Johnson and J. A. Beker.

  2. 2.

    The 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. This database contains fully anonymized images from the following institutions: ETH Zurich, University of Bern, University of Debrecen, and University of Utah. Size of each image in the database is \(256 \times 256\).

References

  1. Deangelis, L.M.: Brain tumors. New Engl. J. Med. 344, 114–123 (2001)

    Article  Google Scholar 

  2. Wen, P.Y., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neurooncology working group. J. Clin. Oncol. 28, 1963–1972 (2010)

    Article  Google Scholar 

  3. Jui, S.L., Zhang, S., Xiong, W., Yu, F., Fu, M., Wang, D., Hassanien, A.E.: Brain MR image tumor segmentation with 3-Dimensional intracranial structure deformation features. IEEE Intell. Syst. submitted, under review (2015)

    Google Scholar 

  4. Clarke, L.P., Velthuizen, R.P., Camacho, M.A., Heine, J.J., Vaidyanathan, M., Hall, L.O., Thatcher, R., Silbiger, M.L.: MRI segmentation: methods and applications. Neuroanatomy 11(3), 343–368 (1995)

    Google Scholar 

  5. Jui, S.L., Lin, C., Guan, H.B., Abraham, A., Hassanien, A.E., Xiao, K.: Fuzzy C-Means with wavelet filtration for mr image segmentation. In: 6th World Congress on Nature and Biologically Inspired Computing, pp. 12–16 (2014)

    Google Scholar 

  6. Fix, J.D.: Neuroanatomy, 3rd edn. Lippincott Williams Wilkins, Philadelphia (2002)

    Google Scholar 

  7. Bookstein, F.L.: Thin-Plate splines and the decomposition of deformation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989)

    Article  MATH  Google Scholar 

  8. Canny, J.A.: Computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)

    Article  Google Scholar 

  9. Mokhtarian, F., Suomela, R.: Robust image corner detection through curvature scale space. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1376–1381 (1998)

    Article  Google Scholar 

  10. Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Comput. Vis. Image Underst. 89(2–3), 114–141 (2003)

    Article  MATH  Google Scholar 

  11. Xiao, K., Ho, S.H., Hassanien, A.E.: Automatic Unsupervised segmentation methods for mri based on modified fuzzy C-Means. Fundam. Inform. 87(3–4), 465–481 (2008)

    MathSciNet  MATH  Google Scholar 

  12. Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  13. Powers, D.: Evaluation: from precision, recall and F-Measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, China

    Shichen Zhang, Fei Hu, Shang-Ling Jui & Kai Xiao

  2. Scientific Research Group, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Authors
  1. Shichen Zhang
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  2. Fei Hu
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  3. Shang-Ling Jui
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  4. Aboul Ella Hassanien
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  5. Kai Xiao
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Corresponding author

Correspondence to Kai Xiao .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Informatics, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  2. Department of Information Technology, Cairo University Faculty of Computers & Information, Giza, Egypt

    Aboul Ella Hassanien

  3. College of Management and Technology, Arab Academy for Science, Technology, and Maritime Transport, Giza, Egypt

    Nashwa El-Bendary

  4. Bengal College of Engineering and Technology, Durgapur, West Bengal, India

    Nilanjan Dey

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Zhang, S., Hu, F., Jui, SL., Hassanien, A.E., Xiao, K. (2016). Unsupervised Brain MRI Tumor Segmentation with Deformation-Based Feature. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_16

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  • DOI: https://doi.org/10.1007/978-3-319-26690-9_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26688-6

  • Online ISBN: 978-3-319-26690-9

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