CFP last date
15 May 2024
Reseach Article

A Novel Segmentation Approach by the Concept of Stability Analysis

Published on June 2013 by S. Nirmala, V. Royna Daisy
International Conference and workshop on Advanced Computing 2013
Foundation of Computer Science USA
ICWAC - Number 1
June 2013
Authors: S. Nirmala, V. Royna Daisy
3a8e2050-8b6d-468f-a88a-22e450e8e9a0

S. Nirmala, V. Royna Daisy . A Novel Segmentation Approach by the Concept of Stability Analysis. International Conference and workshop on Advanced Computing 2013. ICWAC, 1 (June 2013), 0-0.

@article{
author = { S. Nirmala, V. Royna Daisy },
title = { A Novel Segmentation Approach by the Concept of Stability Analysis },
journal = { International Conference and workshop on Advanced Computing 2013 },
issue_date = { June 2013 },
volume = { ICWAC },
number = { 1 },
month = { June },
year = { 2013 },
issn = 2249-0868,
pages = { 0-0 },
numpages = 1,
url = { /proceedings/icwac/number1/475-1304/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and workshop on Advanced Computing 2013
%A S. Nirmala
%A V. Royna Daisy
%T A Novel Segmentation Approach by the Concept of Stability Analysis
%J International Conference and workshop on Advanced Computing 2013
%@ 2249-0868
%V ICWAC
%N 1
%P 0-0
%D 2013
%I International Journal of Applied Information Systems
Abstract

The most crucial step in the process of image processing is image segmentation. Though different image segmentation algorithms have been developed, yet each method has its own advantages and limitations. In this paper a novel approach for image segmentation has been proposed based on the concept of stability. The concept of stability in terms of modified Sylvester's formula applied in the context of positive definiteness, semi positive definiteness and negative definiteness to satisfy a region of convergence is applied for image segmentation. The proposed methodology is applied for images with distinct homogeneous regions. The segmentation precision is quantified and it is evident through visual inspection.

References
  1. J. Suri, D. Wilson, and S. Laxminarayanan, "Handbook of Biomedical Image Analysis": Volume I,II: Segmentation Models. Kluwer Academic / Plenum Publishers, 2005
  2. S. Osher, J. A. Sethian," Fronts propagating with Curvature dependent speed", J Comp Physics, 79, 1988
  3. J. A. Sethian,"Level set methods and fast marching methods", Cambridge University Press, 1999
  4. P. AYushkevich, J. Piven, H. C. Hazlett, R. G. Smith,S. Ho, J. C. Gee, G. Gerig,"User guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability", NeuroImage, 31(3), 2006, 1116-1128
  5. J. S. Suri, K. Liu, S. Singh, S. Laxminrayanan, X. Zeng ,L. Reden,"Shape Recovery algorithms using level sets in 2D/3D medical imagery:a state of the art review", IEEE Transactions on Information Technology in Biomedicine, Vol. 6, pp. 8-28, 2002
  6. S. Yan, J. Yuan and C. Hou," Segmentation of medical ultrasound images based on level set method with edge representing mask", Advanced Computer Theory and Engineering, 3rd International Conference, Vol. 2, 2010
  7. M. Strumia, D. Feltell, N. Evangelou, P. Gowland, C. Tench and L. Bai,"Gray matter segmentation of 7T MR images, IEEE Nuclear Science Symposium and Medical Imaging Conference, 2011, pp. 3710-3714
  8. Katsuhiko Ogata, "Modern Control Engineering", Prentic Hall, 5th Edition, 2010
  9. M. Gopal, "Modern Control System Theory", New Age International Publishers, 1993
  10. Yixin Chen, James Z. Wang, Robert Krovetz, "Content Based Image Retrieval by Clustering",Proc of the 5th ACM SIGMM I'ntl workshop on Multimedia information retrieval ,New York,ACM press,pp-193-200,2003.
  11. Huiyu Zhou, Abdul H. Sadka, Mohammad R. Swash, JawidAzizi and Abubakar S. Umar. , "Content Based Image Retrieval and Clustering: A Brief Survey" school of Engineering and Design, Brunel University, Uxbridge, UB8 3PH, UK
  12. Wang JZ, Li J, Wiederhold G. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans pattern Analysis Machine Intell;23:947-963, 2001.
  13. Jin J, Kurniawati R, Xu G, Bai X. Using browsing to improve content-based image retrieval. J Visual Common Image Represent;12:123-135, 2001.
  14. Huang Min,Sunbo,XiJianqing"An Optimized image retrieval method based on Hierarchical clustering and genetic algorithm"I'ntl forum on Information technology and applications,978-0-7695-3600-2/09-IEEE,2009
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Stability Positive definite Semi definite Negative definite Region of Convergence