Disjoint Tree Based Clustering and Merging for Brain Tumor Extraction

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Several application areas like medical, geospatial and forensic science use variety of clustering approaches for better analysis of the subject matter. In this paper a new hierarchical clustering algorithm called Disjoint Tree Based Clustering and Merging, is proposed which clusters the given dataset on the basis of initially generating maximum possible disjoint trees followed by tree merging. Proposed algorithm is not domain specific and be used for both data points and image. For the result analysis, the algorithm is tested on medical images for clustering of the abnormality region (tumor) from brain MR Images. Proposed algorithm was also compared with the standard K-Means algorithm and the implementation results shows that the proposed algorithm gives significant results for tumor extraction.


Clustering Disjoint Trees Tree Merging Tumor 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Estivill-Castro, V.: Why so many clustering algorithms - A Position Paper. ACM SIGKDD Explorations Newsletter 4(1), 65–75 (2000)CrossRefGoogle Scholar
  2. 2.
    Han, J., Kamber, M.: Data Mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers (2006)Google Scholar
  3. 3.
    Moftah, H.M., Hassanien, A.E., Shoman, M.: 3D Brain Tumor Segmentation Scheme using K-mean Clustering and Connected Component Labeling Algorithms. In: 10th International Conference on Intelligent System and Design Application, pp. 320–324 (2010)Google Scholar
  4. 4.
    Zhang, J., Hu, J.: Image Segmentation Based on 2D Otsu Method with Histogram Analysis. In: International Conference on Computer Science and Software Engineering, pp. 105–108 (2008)Google Scholar
  5. 5.
    Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Journal of Computerized Medical Imaging and Graphics 30(1), 9–15 (2006)CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Nyma, A., Kang, M., Kwon, Y.K., Kim, C.H., Kim, J.M.: A Hybrid Technique for Medical Image Segmentation. Journal of Biomedicine and Biotechnology (2012)Google Scholar
  8. 8.
    Mohamed, N.A., Ahmed, M.N., Farag, A.: Modified fuzzy c-mean in medical image segmentation. In: 20th Annual International Conference on Engineering in Medicine and Biology Society (1998)Google Scholar
  9. 9.
  10. 10.
    Maiti, I., Chakraborty, M.: A new method for brain tumor segmentation based on watershed and edge detection algorithms in HSV colour model. In: National Conference on Computing and Communication Systems (2012)Google Scholar
  11. 11.
    Soleimani, V., Vincheh, F.H.: Improving Ant Colony Optimization for Brain MRI Image Segmentation and Brain Tumor Diagnosis. In: First Iranian Conference on Pattern Recognition and Image Analysis (2013)Google Scholar
  12. 12.
    Vidyarthi, A., Mittal, N.: A Hybrid Model for the Extraction of Brain Tumor in MR Image. In: International Conference on Signal Processing and Communication (2013)Google Scholar
  13. 13.
    Weizman, L., Sira, L.B., Joskowicz, L., Constantini, S., Precel, R., Shofty, B., Bashat, D.B.: Automatic segmentation, internal classification, and follow-up of opticpathway gliomas in MRI. Journal of Medical Image Analysis 16, 177–188 (2012)CrossRefGoogle Scholar
  14. 14.
    Harati, V., Khayati, R., Farzan, A.: Fully Automated Tumor Segmentation based on Improved Fuzzy Connectedness Algorithm in Brain MR Images. Journal of Computers in Biology and Medicine 41, 483–492 (2011)CrossRefGoogle Scholar
  15. 15.
    Khotanlou, H., Colliot, O., Atif, J., Bloch, A.: 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Journal of Fuzzy Sets and System 160, 1457–1473 (2009)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Niethammer, M., Zach, C.: Segmentation with area constraints. Journal of Medical Image Analysis 17, 101–112 (2013)CrossRefGoogle Scholar
  17. 17.
    Solomon, J., Butman, J.A., Sood, A.: Segmentation of Brain Tumors in 4D MR Images using Hidden Markov Model. Journal of Computer Methods and Programs in Biomedicine 84, 76–85 (2006)CrossRefGoogle Scholar
  18. 18.
    Logeswari, T., Karnan, M.: An Improved Implementation of Brain TumorDetection Using Segmentation based onHierarchical Self Organizing Map. International Journal of Computer Theory and Engineering 2(4), 591–595 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Malaviya National Institute of Technology JaipurJaipurIndia

Personalised recommendations