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

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

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

Reference

  • Chien-Cheng Lee, S. Chen and Y. Chiang, 2006. Automatic Liver diseases Diagnosis for CT Images using Kernel based classifiers in IEEE world Automation Congress, pp. 1-6.
  • Poonguzhali, B. Deepalakshmi and G. Ravindran, 2007 Optimal Feature Selection and Automatic Classification of Abnormal Masses in Ultrasound Liver Images in IEEE-ICSCN, pp. 503-506.
  • Rafael C. Gonzalez and Richard E. Woods, 2009. Digital Image Processing, Prentice-Hall.
  • Linda G. Shapiro and George C. Stockman (2001): Computer Vision, pp 279-325, New Jersey, Prentice-Hall, ISBN 0-13-030796-3.
  • C. Bezdek, L. O. Hall and L P. Clarke, 1993. Review of MR image segmentation techniques using pattern recognition in Med. Phys., vol. 20, Issue 4, pp. 1033-1048.
  • Ashish Thakur and Radhey Shyam Anand, 2010. A Local Statistics Based region Growing Segmentation Method for ultrasound Medical Images in Jr. of World Academy of Science, Engg. and Technology, pp. 321-326.
  • Madabhushi, A. and Mtaxas, D. N., 2003. Combining low-,high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions in IEEE transactions on medical imaging, VOL. 22, NO. 2.
  • Eslami, A.; Kasaei, S.; Jahed, M., 2004. Radial multiscale cyst segmentation in ultrasound images of kidney in 4th IEEE International Symposium on Signal Processing and Information Technology, pp. 42-45.
  • Pavlidis, T., and Liow, Y. T., 1990. Integrating region growing and edge detection”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 12, pp. 225-231.
  • XU D, GUO S. 2009. Research Progress of Liver CT Image Segmentation Techniques in Chinese Medical Equipment Journal•Vol.30 No.3.
  • Yi X., Zhong L. and Lin J., 2010. Liver CT Image Segmentation by Local Entropy Method in International Conf. on computer application and modelling, pp. VIII-591 –VIII-594.
  • Andrew Moore: K-means and Hierarchical Clustering - Tutorial Slides at http://www.cs.cmu.edu/~awm/tutorials/kmeans.html
  • Brian T. Luke: K-Means Clustering at http://fconyx.ncifcrf.gov/~lukeb/kmeans.html
  • Tariq R.: Clustering at http://www.cs.bris.ac.uk/home/tr1690/documentation/fuzzy_clustering_initial_report.
  • Image Processing Toolbox, available at http://www.caspur.it/risorse/softappl/doc/matlab_help/toolbox/images/morph11.html#34328.
  • L. Kaur, R.C. Chauhan, and S. C. Saxena, 2006. Wavelet based Compression of Medical Ultrasound images using Vector Quantization in Taylor and Francis JMET, pp. 128-133, vol. 30, no. 3.
  • S. Gupta, L. Kaur, R.C. Chauhan, and S. C. Saxena, 2007. A versatile Technique for visual enhancement of medical ultrasound images in Elsevier Jr. of Digital Signal Processing, 17, pp. 542-560.