Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Spatial Fuzzy C-Means Clustering based Segmentation of Tumor in Vertebral Column Images

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
V. Asanambigai, J. Sasikala

V Asanambigai and J Sasikala. Spatial Fuzzy C-Means Clustering based Segmentation of Tumor in Vertebral Column Images. International Journal of Computer Applications 152(1):37-40, October 2016. BibTeX

	author = {V. Asanambigai and J. Sasikala},
	title = {Spatial Fuzzy C-Means Clustering based Segmentation of Tumor in Vertebral Column Images},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2016},
	volume = {152},
	number = {1},
	month = {Oct},
	year = {2016},
	issn = {0975-8887},
	pages = {37-40},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2016911760},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Precise detection and segmentation of tumor that represents uncontrolled growth of tissue, in vertebral column is complex due to vertebrae shapes, gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the MRI or CT scan images, and becomes a challenging task. This paper presents an elegant method using FCM clustering with spatial information for segmenting tumor region of such images. It segments the cluster image corresponding to the largest centroid and uses a few of the morphological operations for removing unwanted regions. It includes the results of three test images for illustrating the goodness of the proposed method.


  1. Yao J, O’Connor SD, Summers RM. (2006). Automated spinal column extraction and partitioning. In Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, Arlington, VA, 390–393
  2. Hahn M, Beth T. (2004) Balloon based vertebra separation in CT images. In Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, Los Alamitos, CA, 310–315
  3. Ruiz D, Berenguer V, Soriano A, SáNchez B. (2011). A decision support system for the diagnosis of melanoma: A comparative approach. Expert Systems with Applications, 38(12):217-23.
  4. A. Suzani, A. Rasoulian, S. Fels, R. N. Rohling, and P. Abolmaesumi. (2014). Semi-automatic segmentation of vertebral bodies in volumetric mr images using a statistical shape-pose model, Proc. SPIE 9036, Medical Imaging, vol. 9036, 1- 6.
  5. T. C. Mann RS, Constantinescu CS. (2007). Upper cervical spinal cord cross-sectional area in relapsing remitting multiple sclerosis: application of a new technique for measuring cross sectional area on magnetic resonance images, J Magn Reson Imaging, 26: 61–67.
  6. C. M. Schmit BD. (2004) Quantification of morphological changes in the spinal cord in chronic spinal cord injury using magnetic resonance imaging, IEEE Eng Med Biol Soc, 6: 4425–4453,.
  7. P. G. B. G. M. D. A. S. Coulon O, Hickman SJ. (2002). Quantification of spinal cord atrophy from magnetic resonance images via a b-spline active surface model. Magn Reson Med, 47: 1176–1185.
  8. W,.Cai, S. Chen, D. Zhang, Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation, Pattern Recognition 40 (2007) 825-838.
  9. W.K.Lei, B.N. Li, M.C.Dong, M.I.VAi. (2007). AFC-ECG an adaptive fuzzy ECG classifier, in Proceedings of the 11th World Congress on Soft Computing in Industrial Applications (WSC11), Advances in Soft Computing 39: 189-199.
  10. K. S. Chuang, H. L. Hzeng, S. Chen, J. Wu, T. J. Chen. (2006). Fuzzy c-means clustering with spatial information for image segmentation, Computerized medical imaging and graphics, 30: 9-15.
  11. V. Asanambigai, J. Sasikala, Adaptive chemical reaction based spatial fuzzy clustering for level set segmentation of medical images, Ain Shams Engineering Journal,


fuzzy c-means clustering, image segmentation.