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HSV Based Histogram Thresholding Technique for MRI Brain Tissue Segmentation

  • T. Priya
  • P. KalavathiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 968)

Abstract

Background: To bring it as a human interactive perceive color process, an automatic color model based segmentation of White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) in Magnetic Resonance Brain images is proposed in this paper.

Methods: Preprocessing process is done for the MRI brain images using wavelet based bivariate shrinkage method and Contour based Brain Segmentation method (CBSM). Then segmentation of brain tissues using Hue Saturation Value (HSV) color model Based Histogram Thresholding Technique (HSVBHTT) was applied. Normal and Alzheimer’s disease (AD) brain images obtained from Internet Brain Segmentation Repository (IBSR) and Minimal Interval Resonance Imaging in Alzheimer’s Disease (MIRIAD) datasets.

Results and Conclusions: The results of proposed method was analyzed with similarity measures and quantitative measures like Jaccard (J), Dice (D), Sensitivity (S) and Specificity (SP) and compared with the manual segmented images which produced better results on segmenting WM, GM and CSF compared to other existing methods.

Keywords

Alzheimer’s Disease Brain tissue segmentation Histogram Thresholding HSV color model 

Notes

Acknowledgement

This work was supported by Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India.

References

  1. 1.
    Kalavathi, P., Surya Prasath, V.B.: Methods on skull stripping of MRI head scans images - a review. J. Digit. Imaging 29, 365–379 (2016)CrossRefGoogle Scholar
  2. 2.
    Kalavathi, P., Priya, T.: Performance of clustering techniques on segmentation of brain tissues in MRI human head scans. In: Proceedings of National Conference on New Horizons in Computational Intelligence and Information Systems, pp. 164–170. Excel India Publications, New Delhi (2015)Google Scholar
  3. 3.
    Kalavathi, P., Priya, T.: MRI brain tissue segmentation using AKM and FFCM clustering techniques. In: Proceedings of National Conference on Recent Advances in Computer Science and Application, pp. 113–118. Bonfring Publications, India (2015)Google Scholar
  4. 4.
    Somasundaram, K., Kalavathi, P.: Brain segmentation in magnetic resonance human head scans using multi-seeded region growing. Imaging Sci. J. 62(5), 273–284 (2014)CrossRefGoogle Scholar
  5. 5.
    Somasundaram, K., Kalavathi, P.: A novel skull stripping technique for T1-weighted MRI human head scans, pp. 1–8. ACM Digital Library (2012)Google Scholar
  6. 6.
    Somasundaram, K., Kalavathi, P.: Skull stripping of MRI head scans based on chan-vese active contour model. Int. J. Knowl. Manag. E-Learn. 3(1), 7–14 (2011)Google Scholar
  7. 7.
    Kalavathi, P.: Brain tissue segmentation in MR brain images using Otsu’s multiple thresholding technique, pp. 638–642. IEEE Xplore Digital Library (2013)Google Scholar
  8. 8.
    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  9. 9.
    Kalavathi, P., Priya, T.: Brain extraction from MRI human head scans using outlier detection based morphological operation. Int. J. Comput. Sci. Eng. 6(4), 266–273 (2018)Google Scholar
  10. 10.
    Aslam, A., Khan, E., Beg, M.M.S.: Improved edge detection algorithm for brain tumor segmentation. In: Second International Symposium on Computer Vision and the Internet (VisionNet 2015), vol. 58, pp. 430–437. Elsevier (2015)Google Scholar
  11. 11.
    Somasundaram, K., Kalavathi, P.: A hybrid method for automatic skull stripping of magnetic resonance images (MRI) of human head scans, pp. 1–5. IEEE Xplore Digital Library (2010)Google Scholar
  12. 12.
    Renjith, A., Manjula, P., Mohan Kumar, P.: Brain tumor classification and abnormality detection using neuro-fuzzy technique and Otsu thresholding. J. Med. Eng. Technol. 39(8), 498–507 (2015)CrossRefGoogle Scholar
  13. 13.
    Attique, M., et al.: Colorization and automated segmentation of human T2 MR brain images for characterization of soft tissue. PLOS ONE 7(3), 1–13 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhong, S.D., Wei, Y.K., Xie, Z.G.: Method of automatic tongue area extraction in tooth-marked tongue images. Comput. Technol. Dev. 19(1), 245–247 (2009)Google Scholar
  15. 15.
    Lalaoui, L., Mohamadi, T., Djaalab, A.: New method for image segmentation. Soc. Behav. Sci. 195, 1971–1980 (2015). World Conference on Technology, Innovation and EntrepreneurshipCrossRefGoogle Scholar
  16. 16.
    Qin, Z., Wang, F., Xiao, Z., Lan, T., Ding, Y.: Brain tissue segmentation with the GKA method in MRI. In: IEEE International Conference on Signal and Image Processing, pp. 273–276 (2016)Google Scholar
  17. 17.
    Roy, S., Bandyopadhyay, S.K.: A new method of brain tissues segmentation from MRI with accuracy estimation. In: International Conference on Computational Modeling and Security, vol. 85, pp. 362–369. Elseveir (2016)Google Scholar
  18. 18.
    Sulaiman, S.N., Non, N.A., Isa, I.S., Hamzah, N.: Segmentation of brain MRI image based on clustering algorithm. In: Energy, Environment, Biology and Biomedicine, pp. 54–59 (2014)Google Scholar
  19. 19.
    Kannan, S.R., Sathya, A., Ramathilagam, S., Devi, R.: Novel segmentation algorithm in segmenting medical images. J. Syst. Softw. 83, 2487–2495 (2010)CrossRefGoogle Scholar
  20. 20.
    Ganesan, P., Chakravarty, P., Verma, S.: Segmentation of natural color images in HSI color space based on FCM clustering. Int. J. Adv. Res. Comput. Eng. Technol. 3(3), 618–622 (2014)Google Scholar
  21. 21.
    Verma, R., Singh Rathore, S., Verma, A.: MRI segmentation using K-Means clustering in HSV transform. Int. J. Adv. Res. Comput. Eng. Technol. 4(10), 3925–3929 (2015)Google Scholar
  22. 22.
    Mandal, A.K., Baruah, D.K.: Image segmentation using local thresholding and Ycbcr color space. Int. J. Eng. Res. Appl. 3(6), 511–514 (2013)Google Scholar
  23. 23.
    Bora, D.J., Gupta, A.K., Khan, F.A.: Comparing the performance of L*A*B and HSV color spaces with respect to color image segmentation. Int. J. Emerg. Technol. Adv. Eng. 5(2), 193–203 (2015)Google Scholar
  24. 24.
    Duan, J., Yu, L.: A WBC segmentation method based on HSI color space. In: Fourth IEEE International Conference on Broadband Network and Multimedia Technology, pp. 629–632 (2011)Google Scholar
  25. 25.
    Sharma, P., Abrol, P.: Color based image segmentation using adaptive thresholding. Int. J. Sci. Tech. Adv. 2(3), 151–156 (2016)Google Scholar
  26. 26.
    Harrabi, R., Ben Braiek, E.: Color image segmentation based on a modified fuzzy C means technique and statistical features. Int. J. Comput. Eng. Res. 2(1), 120–135 (2012)Google Scholar
  27. 27.
    Kalavathi, P., Priya, T.: Segmentation of brain tissue in MR brain image using wavelet based image fusion with clustering technique. In: Proceedings of National Conference on Computational Methods, Communication Techniques and Informatics, pp. 28–33 (2017)Google Scholar
  28. 28.
    Kalavathi, P., Priya, T.: Noise removal in MR brain images using 2D wavelet based bivariate shrinkage method. Glob. J. Pure Appl. Math. 13(5), 77–86 (2017)Google Scholar
  29. 29.
    Somasundaram, K., Kalavathi, P.: Contour-based brain segmentation method for magnetic resonance imaging human head scans. J. Comput. Assist. Tomogr. 37(3), 353–368 (2013)CrossRefGoogle Scholar
  30. 30.
    Maiti, I., Chakraborty, M.: A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV color model. In: Proceedings of National Conference on Computing and Communication Systems (2012)Google Scholar
  31. 31.
  32. 32.
  33. 33.
  34. 34.
    Anbeek, P., Vincken, K.L., Groenendaal, F., Koeman, A., van Osch, M.J.P., Grond, J.V.D.: Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging. Pediatr. Res. 63(2), 158–163 (2008)CrossRefGoogle Scholar
  35. 35.
  36. 36.
    Shantha kumar, P., Ganesh kumar, P.: Performance analysis of brain tumor diagnosis based on soft computing technique. Am. J. Appl. Sci. 11(2), 329–336 (2014)Google Scholar
  37. 37.
    Chui, H.C., Gomez, L.R.: Clinical and imaging features of mixed Alzheimer and vascular pathologies. Alzheimer’s Res. Ther. 7(1), 21 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and ApplicationsThe Gandhigram Rural Institute - (Deemed to be University)GandhigramIndia

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