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Multi-modal Brain Tumor Segmentation Based on Self-organizing Active Contour Model

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Pattern Recognition (CCPR 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

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Abstract

In this paper, an automatic and practical method based on active contour model (ACM) is proposed for multi-modal brain tumor segmentation. Firstly, we construct a concurrent self-organizing map (CSOM) networks. Then, applying the networks into a local region based ACM framework constructs a SOM based ACM, i.e. self-organizing active contour model (SOAC). Finally, by using SOAC, making tumor segmentation problems to be stated as a process of contour evolution. However, the segmentation task cannot be well performed for single-modal MRI images due to intensity similarities between brain normal tissues and lesions. For highlighting different tissues, between normal and abnormal, using multi-modal MRI information is an effective way to improve segmentation accuracy, obviously. Therefore, we introduce a global difference strategy, which creates a series of difference images from multi-modal MRI images, namely global difference images (GDI). By reorganizing MRI images and GDI, we propose an automatic segmentation method for brain tumor region extraction with multi-modal MRI images based on SOAC. The effectiveness of the method is tested on the real data from BRATS2013 and part of BRATS2015.

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References

  1. Kharrat, A., Benamrane, N., Ben Messaoud, M., et al.: Detection of brain tumor in medical images. In: IEEE 3rd International Conference on Signals, Circuits and Systems, pp. 1–6 (2009)

    Google Scholar 

  2. Lee, M., Street, W.: Dynamic learning of shapes for automatic object recognition. In: 17th Workshop Machine Learning of Spatial Knowledge, pp. 44–49 (2000)

    Google Scholar 

  3. Tang, H., Wu, E.X., Ma, Q.Y., et al.: MRI brain image segmentation by multi-resolution edge detection and region selection. Comput. Med. Imaging Graph. 24(6), 349–357 (2000)

    Article  Google Scholar 

  4. Prastawa, M., Bullitt, E., Ho, S., et al.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8(3), 275–283 (2004)

    Article  Google Scholar 

  5. Islam, A., Reza, S., Iftekharuddin, K.M.: Multifractal texture estimation for detection and segmentation of brain tumors. IEEE Trans. Biomed. Eng. 60(11), 3204–3215 (2013)

    Article  Google Scholar 

  6. Menze, B., Reyes, M., Van Leemput, K., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)

    Google Scholar 

  7. Prastawa, M., Bullitt, E., Moon, N., et al.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Acad. Radiol. 10(12), 1341–1348 (2003)

    Article  Google Scholar 

  8. Zacharaki, E.I., Wang, S., Chawla, S., et al.: Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reason. Med. 62(6), 1609–1618 (2009)

    Article  Google Scholar 

  9. White, D.R.R., Houston, A.S., Sampson, W.F.D., et al.: Intra and inter-operator variations in region-of-interest drawing and their effect on the measurement of glomerular filtration rates. Clin. Nucl. Med. 24(3), 177–181 (1999)

    Article  Google Scholar 

  10. Olabarriaga, S.D., Smeulders, A.W.M.: Interaction in the segmentation of medical images: a survey. Med. Image Anal. 5(2), 127–142 (2001)

    Article  Google Scholar 

  11. Foo, J.L.: A survey of user interaction and automation in medical image segmentation methods. Iowa State University, Human Computer Interaction Technical report ISU-HCI-2006-02 (2006)

    Google Scholar 

  12. Ho, S., Bullitt, L., Gerig, G.: Level-set evolution with region competition: automatic 3-D segmentation of brain tumors. In: IEEE 16th International Conference on Pattern Recognition, vol. 1, pp. 532–535 (2002)

    Google Scholar 

  13. Vijayakumar, C., Gharpure, D.C.: Development of image-processing software for automatic segmentation of brain tumors in MR images. J. Med. Phys Assoc. Med. Physicists India 36(3), 147 (2011)

    Google Scholar 

  14. Wang, Y., Lin, Z.X., Cao, J.G., et al.: Automatic MRI brain tumor segmentation system based on localizing active contour models. Adv. Mater. Res. 219, 1342–1346 (2011)

    Google Scholar 

  15. Lee, C.P., Snyder, W., Wang, C.: Supervised multispectral image segmentation using active contours. In: IEEE International Conference on Robotics and Automation, pp. 4242–4247 (2005)

    Google Scholar 

  16. Nabizadeh, N., Kubat, M.: Brain tumors detection and segmentation in MR images. Gabor wavelet vs. statistical features. Comput. Electr. Eng. 45, 286–301 (2015)

    Google Scholar 

  17. Popuri, K., Cobzas, D., Murtha, A., et al.: 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int. J. Comput. Assist. Radiol. Surg. 7(4), 493–506 (2012)

    Article  Google Scholar 

  18. Deng, W., Xiao, W., Deng, H., et al.: MRI brain tumor segmentation with region growing method based on the gradients and variances along and inside of the boundary curve. In: IEEE 3rd International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1, pp. 393–396 (2010)

    Google Scholar 

  19. Phillips, W.E., Velthuizen, R.P., Phuphanich, S., et al.: Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn. Reason. Imaging 13(2), 277–290 (1995)

    Article  Google Scholar 

  20. Siyal, M.Y., Yu, L.: An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI. Pattern Recogn. Lett. 26(13), 2052–2062 (2005)

    Article  Google Scholar 

  21. Kannan, S.R.: A new segmentation system for brain MR images based on fuzzy techniques. Appl. Soft Comput. 8(4), 1599–1606 (2008)

    Article  Google Scholar 

  22. Cates, J.E., Whitaker, R.T., Jones, G.M.: Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med. Image Anal. 9(6), 566–578 (2005)

    Article  Google Scholar 

  23. Lee, C.-H., Schmidt, M., Murtha, A., Bistritz, A., Sander, J., Greiner, R.: Segmenting brain tumors with conditional random fields and support vector machines. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 469–478. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  24. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20(1), 45–57 (2001)

    Article  Google Scholar 

  25. Held, K., Kops, E.R., Krause, B.J., et al.: Markov random field segmentation of brain MR images. IEEE Trans. Med. Imaging 16(6), 878–886 (1997)

    Article  Google Scholar 

  26. Özkan, M., Dawant, B.M., Maciunas, R.J.: Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study. IEEE Trans. Med. Imaging 12(3), 534–544 (1993)

    Article  Google Scholar 

  27. Yan, L.I.: A survey on active contour models for image segmentation. Remote Sens. Inf. 1, 233–236 (2014)

    Google Scholar 

  28. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Google Scholar 

  29. Li, C., Xu, C., Gui, C., et al.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)

    Article  MathSciNet  Google Scholar 

  30. Liu, S., Peng, Y.: A local region-based Chan-Vese model for image segmentation. Pattern Recogn. 45(7), 2769–2779 (2012)

    Article  MATH  Google Scholar 

  31. Abdelsamea, M.M., Gnecco, G.: Robust local–global SOM-based ACM. Electron. Lett. 51(2), 142–143 (2015)

    Article  Google Scholar 

  32. Abdelsamea, M.M., Gnecco, G., Gaber, M.M.: An efficient self-organizing active contour model for image segmentation. Neuro-Computing 149, 820–835 (2015)

    Google Scholar 

  33. Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)

    Article  MATH  Google Scholar 

  34. Neagoe, V.E., Ropot, A.D.: Concurrent self-organizing maps for pattern classification. In: IEEE 1st International Conference on Cognitive Informatics, pp. 304–312 (2002)

    Google Scholar 

  35. Loizou, C.P., Pantziaris, M., Seimenis, I., et al.: Brain MRI image normalization in texture analysis of multiple sclerosis. In: IEEE 9th International Conference on Information Technology and Applications in Biomedicine, pp. 1–5 (2009)

    Google Scholar 

  36. Yushkevich, P.A., Piven, J., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006)

    Article  Google Scholar 

  37. Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Trans. Image Process. 17(11), 2029–2039 (2008). A Publication of the IEEE Signal Processing Society

    Google Scholar 

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Acknowledgments

This work was supported by the National Science Foundation of China (NO. 61671125 and NO. 61201271), and the State Key Laboratory of Synthetical Automation for Process Industries (NO. PAL-N201401).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Rui Liu, Jian Cheng, Xiaoya Zhu, Hao Liang & Zezhou Chen

Authors
  1. Rui Liu
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  2. Jian Cheng
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  3. Xiaoya Zhu
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  4. Hao Liang
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  5. Zezhou Chen
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Corresponding author

Correspondence to Jian Cheng .

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Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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Liu, R., Cheng, J., Zhu, X., Liang, H., Chen, Z. (2016). Multi-modal Brain Tumor Segmentation Based on Self-organizing Active Contour Model. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_40

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_40

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