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Interactive Semi-automated Method Using Non-negative Matrix Factorization and Level Set Segmentation for the BRATS Challenge

  • Dimah Dera
  • Fabio Raman
  • Nidhal Bouaynaya
  • Hassan M. Fathallah-ShaykhEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

Abstract

The 2016 BRATS includes imaging data on 191 patients diagnosed with low and high grade gliomas. We present a novel method for multimodal brain segmentation, which consists of (1) an automated, accurate and robust method for image segmentation, combined with (2) semi-automated and interactive multimodal labeling. The image segmentation applies Non-negative Matrix Factorization (NMF), a decomposition technique that reduces the dimensionality of the image by extracting its distinct regions. When combined with the level-set method (LSM), NMF-LSM has proven to be an efficient method for image segmentation. Segmentation of the BRATS images by NMF-LSM is computed by the Cheaha supercomputer at the University of Alabama at Birmingham. The segments of each image are ranked by maximal intensity. The interactive labeling software, which identifies the four targets of the challenge, is semi-automated by cross-referencing the normal segments of the brain across modalities.

Keywords

Gray Matter Image Segmentation High Performance Computing Flair Image Interactive Software 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bayar, B., Bouaynaya, N., Shterenberg, R.: Probabilistic non-negative matrix factorization: theory and application to microarray data analysis. J. Bioinform. Comput. Biol. 12, 25 (2014)CrossRefGoogle Scholar
  2. 2.
    Cohen, L.D., Cohen, I.: Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1131–1147 (1993)CrossRefGoogle Scholar
  3. 3.
    Dera, D., Bouaynaya, N., Fathallah-Shaykh, H.M.: Level set segmentation using non-negative matrix factorization of brain MRI images. In: IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, D.C., pp. 382–387 (2015)Google Scholar
  4. 4.
    Dera, D., Bouaynaya, N., Fathallah-Shaykh, H.M.: Automated robust image segmentation: level set method using non-negative matrix factorization with application to brain mri. Bull. Math. Biol. 78, 1–27 (2016)CrossRefzbMATHGoogle Scholar
  5. 5.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–793 (1999)CrossRefGoogle Scholar
  6. 6.
    Menze, B., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)CrossRefGoogle Scholar
  7. 7.
    Tsai, A., Yezzi, A.S., Willsky, A.: Curve evolution implementation of the mumford-shah functional for image segmentation, denoising, interpolation and magnification. IEEE Trans. Image Process. 10, 1169–1186 (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Dimah Dera
    • 1
  • Fabio Raman
    • 2
  • Nidhal Bouaynaya
    • 1
  • Hassan M. Fathallah-Shaykh
    • 2
    • 3
    • 4
    • 5
    Email author
  1. 1.Department of Electrical and Computer EngineeringRowan UniversityGlassboroUSA
  2. 2.Department of Biomedical EngineeringUniversity of Alabama at BirminghamBirminghamUSA
  3. 3.Department of NeurologyUniversity of Alabama at BirminghamBirminghamUSA
  4. 4.Department of Electrical EngineeringUniversity of Alabama at BirminghamBirminghamUSA
  5. 5.Department of MathematicsUniversity of Alabama at BirminghamBirminghamUSA

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