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Image Segmentation using Canny Edge and finding the Tumor Area in Image using Hierarchical Clustering

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Bandana Bali, Brij Mohan Singh

Bandana Bali and Brij Mohan Singh. Image Segmentation using Canny Edge and finding the Tumor Area in Image using Hierarchical Clustering. International Journal of Computer Applications 167(4):9-12, June 2017. BibTeX

	author = {Bandana Bali and Brij Mohan Singh},
	title = {Image Segmentation using Canny Edge and finding the Tumor Area in Image using Hierarchical Clustering},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {4},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {9-12},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017914234},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Image segmentation of Brain MRI holds great significance in the determination of valuable functional and anatomical information of a disease like tumors. It not only advances the diagnostic techniques but also proves to be of enormous facilitation in the planning of treatment. In this research paper, we will be utilizing the bilateral filter technique to eliminate noise from the brain magnetic resonance imaging images, following by applying the improved canny edge detection algorithm for image segmentation to locate the ridges of tumor areas in them. The last step of hierarchical clustering algorithm application will aid in highlighting the affected area in the images thereby addressing the issues of clear location of tumor cells in the brain MRI images.


  1. Vasupradha Vijay et al. “Automated Brain Tumor Segmentation and Detection in MRI using Enhanced Darwinian Particle Swarm Optimization (EDPSO)” © 2016 Published by Elsevier B.V.
  2. Hala Ali et al. “Multi-resolution MRI Brain Image Segmentation Based on Morphological Pyramid and Fuzzy C-mean Clustering”. Arab J Sci Eng. DOI 10.1007/s13369-015-1791-x.
  3. Archana M et al. “A Hybrid Approach to Automated Delineation of Brain Tissue in Alzheimer MR Images”. 978-0-7695-4964-4/13 $26.00 © 2013 IEEE. DOI 10.1109/NEBEC.2013.39.
  4. Jothi G. et al. “Hybrid Tolerance Rough Set–Firefly based supervised feature selection for MRI brain tumor image classification”. j.asoc.2016.03.014. 1568-4946/© 2016 Elsevier B.V. All rights reserved.
  5. S. Jansi et al. “Modified FCM using Genetic Algorithm for Segmentation of MRI Brain Images”. 978-1-4799-3975-6/14/$31.00 ©2014 IEEE. 2014 IEEE International Conference on Computational Intelligence and Computing Research.
  6. Yamini Sharma et al. “Brain Tumor Extraction From MRI Image Using Mathematical Morphological Reconstruction”. 978-1-4799-6986-9/14/$31.00©2014 IEEE.
  7. Luciano Nieddu et al. “Automatic 3D Image Segmentation Using Adaptive k-means on Brain MRI”. A. Abd Manaf et al. (Eds.): ICIEIS 2011, Part II, CCIS 252, pp. 171–183, 2011. c Springer-Verlag Berlin Heidelberg 2011.
  8. Soham Sarkar et al. “Multi-Level Thresholding with a Decomposition-based Multi- Objective Evolutionary Algorithm for Segmenting Natural and Medical Images”. DOI:
  9. Ji Hoon Kim et al. in their research work “Using a Method Based on a Modified K-Means Clustering and Mean Shift Segmentation to Reduce File Sizes and Detect Brain Tumors from Magnetic”. DOI 10.1007/s11277-016-3420-8. Springer Science+Business Media New York 2016.
  10. D. De Sieno, "Adding a conscience to competitive learning", Proceeding of IEEE the Second International Conference on Neural networks(ICNN88), vol. 1, pp. 117-124, 1988.
  11. D. E. Goldberg, Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Longman Pte. Ltd., pp. 60-68, 2000.
  12. Jayaram K. Udupa, Punam K. Saha, "Fuzzy Connectedness and Image Segmentation", Proceedings of the IEEE, vol. 91, 2003.
  13. M Karnan, R Sivakumar, M Almelumangi, K Selvanayaki, T Logeswari, "Automatic Detection of the Suspicious Regions on Digital Brains Using Genetic Algorithm", Proceedings of National Conference and Workshop on Soft and Intelligent Computing, pp. 23-25, 2008.
  14. Y. Ge, Q. C. Meng, C. J. Yan, J. Xu, "A Hybrid Ant Colony Algorithm for Global Optimization of Continuous Multi-Extreme Functions", Proceedings of the Third International Conference on Machine Learning and Cybernetics, pp. 2427-2432, 2004.
  15. S Murugavallil, V Rajamani, "An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique", Journal of Computer Science, vol. 3, no. 11, pp. 841-846, 2007.
  16. P. Tamije;V. Palanisamy; T. Purusothaman: Performance Analysis of Clustering Algorithms in Brain Tumor Detectionof MR Images European Journal of Scientific Research, ISSN1450-216X Vol. 62 No. 3 2011, pp. 321-330.
  17. S. K. Bandyopadhyay and D. Saha, Brain region extraction volume calculation UNIASCIT, 1, no. 1, pp. 44-48, 2011.
  18. S. Datta; M. Chakraborty. Brain Tumor Detection from Pre-Processed MR Images using SegmentationTechniques. Special Issue on 2nd National Conference-Computing, Communication and Sensor NetworkCCSN Published by Foundation of Computer Science, NewYork, USA. vol. 2, pp. 1-5, 2011.
  19. Kalaiselvi, T. and K. Somasundaram. Fuzzy c-means technique with histogram based centroid initialization forbrain tissue segmentation in MRI of head scans. IEEE International Symposium on Humanities, Science &Engineering Research SHUSER, pp. 149-154, 2011.
  20. Janki Naik , Prof Sagar Patel , Tumor Detection and Classification using Decision Tree in Brain MRI, IJEDR, ISSN:2321-9939, 2013.
  21. K. Somasundaram, T. Kalaiselvi, Automatic brain extractionmethods for T1 magnetic resonance images using regionlabeling and morphological operations, ELSEVIER, Computersin Biology and Medicine 41 2011 716-725
  22. E. A. El-Dihshan, T, Hosney, A B. M. Salem, Hybridinte lligence techniques for MRI Brain images classification,ELSEVIER, Digital Signal Processing, Volume 20, pp,433-441,2010
  23. Jin Liu, Min Li, Jianxin Wang, Fangxiang Wu, Tianming Liu, Yi Pan, A Survey of MR I-Based Brain Tumor Segmentation Methods Tsinghua Science and Technology, vol. 19, no. 6, December 2014.
  24. J. M. P. Gupta, M. M. Shringirishi, "Implementation of brain tumor segmentation in brain MR images using k-means clustering and fuzzy c-means algorithm", International Journal of Computers & Technology, vol. 5, no. 1, pp. 54-59, 2013.
  25. G.-C. Lin, W.-J. Wang, C.-C. Kang, C.-M. Wang, "Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing", Magnetic Resonance Imaging, vol. 30, no. 2, pp. 230-246, 2012.
  26. Prakash Mahindrakar, Dr. M. Hanumanthappa, "Data Mining in Healthcare: A Survey of Techniques and Algorithms with Its Limitations and Challenges", Int. Journal of Engineering Research and Applications, vol. 3, no. 6, pp. 937-941, Nov-Dec 2013, ISBN 2248-9622.


Brain, MRI, Magnetic Resonance Imaging, Segmentation, Algorithm, Tumor, Highlight.