MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique

  • P. Kanmani
  • P. Marikkannu
Image & Signal Processing
Part of the following topical collections:
  1. Convergence of Deep Machine Learning and Nature Inspired Computing Paradigms for Medical Informatics


Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from Magnetic Resonance (MR) brain image is significant concern but a dreary and time-consuming task performed by radiologists or clinical experts, and the final classification accuracy depends on their experience only. To overcome these limitations, it is necessary to use computer-aided techniques. To improve the efficiency of classification accuracy and reduce the recognition complexity involves in the medical image segmentation process, we have proposed Threshold Based Region Optimization (TBRO) based brain tumor segmentation. The experimental results of proposed technique have been evaluated and validated for classification performance on magnetic resonance brain images, based on accuracy, sensitivity, and specificity. The experimental results achieved 96.57% accuracy, 94.6% specificity, and 97.76% sensitivity, shows the improvement in classifying normal and abnormal tissues among given images. Detection, extraction and classification of tumor from MRI scan images of the brain is done by using MATLAB software.


Magnetic Resonance Images Seed points extraction Segmentation TBRO Classification 



No funding was used for this review.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

For this type of review, formal consent is not required. This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Bahadure, N. B., Ray, A. K., and Thethi, H. P., Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging, 2017.
  2. 2.
    Alfonse, M., and Salem, A.-B. M., An automatic classification of brain tumors through MRI using support vector machine. Egypt. Comput. Sci. J. 40(03), September 2016, ISSN: 1110–586.Google Scholar
  3. 3.
    Rezaeil, K., and Agahi, H., Malignant and benign brain tumor segmentation and classification using SVM with weighted kernel width. Signal Image Process. Int. J. (SIPIJ) 8(2):25–36, 2017.CrossRefGoogle Scholar
  4. 4.
    Javadpour, A., and Mohammadi, A., Improving Brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. J. Biomed. Phys. Eng. 6(2):95–108, 2016.Google Scholar
  5. 5.
    Csurka, G., Dance, C. R., Fan, L., Willamowski, J., and Bray, C., Visual categorization with bags of keypoints. In: Proc. Workshop Statistical Learning in Computer Vision, ECCV, vol. 1, pp. 1–22, 2004.Google Scholar
  6. 6.
    Ferrari, V., Tuytelaars, T., and Gool, L. J. V., Simultaneous object recognition and segmentation by image exploration. In: Proc. 8th Eur. Conf. Computer Vision, Part I, Prague, Czech Republic, May 11–14, pp. 40–54, 2004.Google Scholar
  7. 7.
    Muja, M., and Lowe, D. G., Fast approximate nearest neighbors with automatic algorithm configuration. Int. Conf. on Computer Vision Theory and Applications.Google Scholar
  8. 8.
    Liao, X., Yin, J., Guo, S., Li, X., and Sangaiah, A. K., Medical JPEG image steganography based on preserving inter-block dependencies. Comput. Electr. Eng. Elsevier Publishers, 2017.
  9. 9.
    Kanmani, P., and Marikannu, P., An optimal Image retrieval system using content-based image retrieval techniques. Aust. J. Basic Appl. Sci. 9(16):134–139, 2015.Google Scholar
  10. 10.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y., Locality-constrained linear coding for image classification. Computer Vision and Pattern Recognition (CVPR), 2010 I.E. Conference, pp. 3360–3367, June 2010.Google Scholar
  11. 11.
    Dong, R., Wang, H., A novel VHR image change detection algorithm based on image fusion and fuzzy C-means clustering. Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 2017.Google Scholar
  12. 12.
    Anitha, V., and Murugavalli, S., Brain tumor classification using two - tier classifier with adaptive segmentation technique. IET Comput. Vis. 10(1):9–17, 2016.CrossRefGoogle Scholar
  13. 13.
    Du, G., Su, F., and Cai, A., Face recognition using SURF features - MIPPR: pattern recognition and computer vision. Proceedings of SPIE vol. 7496,749628©SPIE·CCCcode:0277-786X/09/$18, 2006.
  14. 14.
    Xiao, F. Y., and Fei, B., A MR brain classification method based on multiscale and multiblock fuzzy C-means. International Conference Bioinform Biomed Eng, pp. 2879–2891, 2011.
  15. 15.
    Liang, R. Z., Shi, L., Wang, H., Meng, J., Wang, J. J. Y., Sun, Q., and Gu, Y., Optimizing top precision performance measure of content-based image retrieval by learning similarity function. Pattern Recognition (ICPR), 23rd IEEE International Conference, pp. 2954–2958, December 2016.Google Scholar
  16. 16.
    Abdel-Basset, M., Fakhry, A. E., El-henawy, I., Qiu, T., and Sangaiah, A. K., Feature and intensity based medical image registration using particle swarm optimization. J. Med. Syst. 41(12):197, 2017. Scholar
  17. 17.
    Rajan, C., and Sountharrajan, S., Metaheuristic optimization technique for feature selection to detect the alzheimer disease from MRI. J. Adv. Res. Dyn. Control Syst. 9(6):1368–1381, 2017.Google Scholar
  18. 18.
    Zhang, R., Shen, J., Wei, F., Li, X., and Sangaiah, A. K., Medical image classification based on multi-scale non-negative sparse coding. Artif. Intell. Med., 2017.
  19. 19.
    Zhang, S., Wang, H., and Huang, W., Two-stage plant species recognition by local mean clustering and weighted sparse representation classification. Clust. Comput. 1–9, 2017.Google Scholar
  20. 20.
    Zheng, H. T., Wang, Z., Ma, N., Chen, J., Xiao, X., and Sangaiah, A. K., Weakly-supervised image captioning based on rich contextual information. Multimed. Tools Appl. 1–17, 2017.
  21. 21.
    Cong Shi, J. Y., Liu, L. Y., Wu, N. J., and Wang, Z. H., A massively parallel key point detection and description (MP-KDD) algorithm for high-speed vision chip. SCIENCE CHINA Inf. Sci. 57(10):1–12, 2014.Google Scholar
  22. 22.
    Kumar, S., Singh, S. K., Abidi, A. I., Datta, D., and Sangaiah, A. K., Group sparse representation approach for recognition of cattle on muzzle point images. Int. J. Parallel Prog. 1–26. 2017.
  23. 23.
    Aly, M., Face recognition using SIFT features. Technical Report, Caltech, USA, 2006.Google Scholar
  24. 24.
    Malek, A. A., Zarina, W. E., and Yasiran, S. S., Seed point selection for seed-based region growing in segmenting microcalcifications. Stat. Sci. Bus. Eng. (ICSSBE), 2012.
  25. 25.
    Wang, Y., Chen, Y., Li, J., and Li, B., The Harris corner detection method based on three scale invariance spaces. IJCSI Int. J. Comput. Sci. Issues 9(6):2, 2012.Google Scholar
  26. 26.
    Boberek, M., and Saeed, K., Segmentation of MRI brain images for automatic detection and precise localization of tumor. Image Process. Commun. Chall. 333–341.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Sri Ramakrishna Institute of TechnologyCoimbatoreIndia
  2. 2.Anna University Regional CentreCoimbatoreIndia

Personalised recommendations