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Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region

Novel Aspects of Digital Imaging Applications
© 2011 by IJCA Journal
ISBN: 978-93-80865-47-9
Year of Publication: 2011
Ramanjot Kaur
Lakhwinder Kaur
Savita Gupta

Ramanjot Kaur, Lakhwinder Kaur and Savita Gupta. Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region. IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA) (1):59–66, 2011. Full text available. BibTeX

	author = {Ramanjot Kaur and Lakhwinder Kaur and Savita Gupta},
	title = {Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region},
	journal = {IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA)},
	year = {2011},
	number = {1},
	pages = {59--66},
	note = {Full text available}


This paper, first analysis the performance of image segmentation techniques; K-mean clustering algorithm and region growing for cyst area extraction from liver images, then enhances the performance of K-mean by post-processing. The K-mean algorithm makes the clusters effectively. But it could not separate out the desired cluster (cyst) from the image. So, to enhance its performance for cyst region extraction, morphological opening-by-reconstruction is applied on the output of K-mean clustering algorithm. The results are presented both qualitatively and quantitatively, which demonstrate the superiority of enhanced K-mean as compared to standard K-mean and region growing algorithm.


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