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

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
V. Asanambigai, J. Sasikala
10.5120/ijca2016911760

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

@article{10.5120/ijca2016911760,
	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 = {http://www.ijcaonline.org/archives/volume152/number1/26284-2016911760},
	doi = {10.5120/ijca2016911760},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

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Keywords

fuzzy c-means clustering, image segmentation.