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Brain tumor detection and segmentation using a wrapper based genetic algorithm for optimized feature set

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Abstract

The work here intends to develop an algorithm for optimizing the available feature set for identifying tumor from brain MRI images. A set of features are selected based on texture features. From the large set of features relevant features would be selected using wrapper approach. Further, an optimized subset of the relevant features is generated with the help of Genetic Algorithm. The machine learning with support vector machine algorithm is used for detection and segmentation of tumors in the brain MRI image acquired. The superiority of the algorithm is established by comparing it with the state of the art algorithms such as level set method and fuzzy based methods. The authors are using performance measurement tools including manual segmentation and volume based tools for validating the claim.

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References

  1. Gonzalez, R.A., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Upper Saddle River (2002)

    Google Scholar 

  2. Dubey, R.B., Hanmandlu, M., Vasikarla, S.: Evaluation of three methods for mri brain tumor segmentation. In: 2011 Eighth International Conference on Information Technology: New Generations, IEEE (2011)

  3. Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. In: Proceedings of the IEEE transaction on Medical Images (2001)

  4. Tolba, M.F., Mostafa, M.G., Gharib, T.F., Salem, M.A.: MR-brain image segmentation using gaussian multi resolution analysis and the EM algorithm. ICEIS 2, 165–170 (2003)

    Google Scholar 

  5. Karnan, M., Logeswari, T.: An improved implementation of braintumor detection using soft computing. Int. J. Comput. Netw. Secur. 2(1), 6–10 (2010)

    Google Scholar 

  6. Kavitha, A.R., Chellamuthu, C.: Detection of brain tumour from MRI image using modified region growing and neural network. Imaging Sci. J. 61(7), 556–567 (2012)

    Article  Google Scholar 

  7. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning, 3rd edn, pp. 60–68. Addison Wesley Longman Pvt. Ltd., Boston (2000)

    Google Scholar 

  8. Sieno, D.D.: Adding a Conscience to Competitive Learning. In: Proceeding of IEEE the Second International Conference on Neural networks (ICNN88), pp. 117–124 (1988)

  9. Sieno, D.D: Adding a Conscience to Competitive Learning. In: Proceeding of IEEE the Second International Conference on Neural networks (ICNN88), pp. 117–124 (1988)

  10. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 3rd edn, pp. 60–68. Addison Wesley Longman Pvt. Ltd., Boston (2000)

    Google Scholar 

  11. Jiang, L., Yang, W. (2003) A modified fuzzy C-means algorithm for segmentation of magnetic resonance images. In: Proc. VIIth Digital Image Computing: Techniques and Applications, Sydney (2003)

  12. Farmer, M.E., Jain, A.K.: A wrapper-based approach toimage segmentation and classification. IEEE Trans. J. Mag. 14(12), 2060–2072 (2005)

    Google Scholar 

  13. Selvathi, D., Anitha, J.: Effective fuzzy clusteringalgorithm for abnormal MR brain image segmentation. In: International/Advance Computing Conference (IACC2009), IEEE (2009)

  14. Kaur, M., Banga, V.K.: Thresholding and level set based brain tumor detection using bounding box as seed. Int. J. Eng. Res. Technol. 4, 2503–2507 (2013)

    Google Scholar 

  15. Wu, J., Ye, F., Ma, J.L., Sun, X.P., Xu, J., Cui, Z.M.: The segmentation and visualization of human organs based on adaptive region growing method. In: IEEE 8th International Conference on Computer and Information Technology Workshops 978-0-7695-3242-4/08, IEEE (2008)

  16. Eschrich, S., Ke, J., Hall, L.O.: Fast accurate fuzzy clustering through data reduction. IEEE Trans. Fuzzy Syst. 11(2), 262–270 (2003)

    Article  Google Scholar 

  17. Menze, B.H., Van Leemput, K., Lashkari, D., Weber, M.-A., Ayache, N., Golland, P.: Segmenting glioma in multi-modal images using a generative model for brain lesion segmentation. In: Proc. MICCAIBRATS, pp. 1–8 (2012)

  18. Zikic, D. et al.: Context-sensitive classification forests for segmentation of brain tumor tissues. In: Proc. MICCAI-BRATS, pp. 22–30 (2012)

  19. Bauer, S., et al.: Segmentation of brain tumor images based on integrated hierarchical classification. In: MICCAI BraTS Workshop. Nice: Miccai Society (2012)

  20. Aswathy, S.U., et al.: Quick detection of brain tumor using a combination of EM and level set method. Indian J. Sci. Technol. 8(34), 74–82 (2015). https://doi.org/10.17485/ijst/2015/v8i34/85361

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Noorul Islam University, Kanyakumari, India

    S. U. Aswathy

  2. Electronics and Instrumentation, Vimal Jyothi Engineering College, Kannur, Kerala, India

    G. Glan Devadhas

  3. Electronics and Instrumentation, Noorul Islam University, Kanyakumari, India

    S. S. Kumar

Authors
  1. S. U. Aswathy
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  2. G. Glan Devadhas
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  3. S. S. Kumar
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Correspondence to S. U. Aswathy.

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Aswathy, S.U., Glan Devadhas, G. & Kumar, S.S. Brain tumor detection and segmentation using a wrapper based genetic algorithm for optimized feature set. Cluster Comput 22 (Suppl 6), 13369–13380 (2019). https://doi.org/10.1007/s10586-018-1914-8

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  • DOI: https://doi.org/10.1007/s10586-018-1914-8

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