Enhanced K-Mean Clustering Algorithm for Liver Image Segmentation to Extract Cyst Region
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10.5120/4159-323 |
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
@article{key:article,
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}
}
Abstract
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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