CFP last date
20 May 2024
Reseach Article

A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation

by Pinaki Pratim Acharjya, Dibyendu Ghoshal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 12
Year of Publication: 2012
Authors: Pinaki Pratim Acharjya, Dibyendu Ghoshal
10.5120/8258-1791

Pinaki Pratim Acharjya, Dibyendu Ghoshal . A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation. International Journal of Computer Applications. 52, 12 ( August 2012), 46-50. DOI=10.5120/8258-1791

@article{ 10.5120/8258-1791,
author = { Pinaki Pratim Acharjya, Dibyendu Ghoshal },
title = { A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 12 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 46-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number12/8258-1791/ },
doi = { 10.5120/8258-1791 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:06.542354+05:30
%A Pinaki Pratim Acharjya
%A Dibyendu Ghoshal
%T A Modified Watershed Segmentation Algorithm using Distances Transform for Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 12
%P 46-50
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a modified watershed algorithm for image segmentation using distances transform and image smoothing method, an improved version of watershed segmentation. This algorithm allows better boundary localization due to the edge information brought by watersheds. Thus, the proposed method has been found to be able to reduce over segmentation and this would ultimately lead to easier handling by the machine towards higher level of processing at subsequent stages. The algorithm has been tested on colored image obtained from real life and has been found to yield satisfactory segmentation results.

References
  1. R. Besl, R. Jain, "Three dimensional object recognition," ACM Computing Surveys, Vol. 17, pp. 75-145, 1985.
  2. P. Suetnes, P. Fua and A. J. Hanson, "Computational strategies for object recognition," ACM Computing Surveys, Vol. 24, pp. 05-61, 1992.
  3. K. Hohne, H. Fuchs, S. Pizer, "3D imaging in medicine: Algorithms, systems, Applications", Berlin, Germany, Springer –Verlag, 1990.
  4. R. Besl, R. Jain, "Three dimensional object recognition," ACM Computing Surveys, Vol. 17, pp. 75-145, 1985.
  5. M. Kunt, M. Bernard and R. Leonardi, "Recent results in high compression image coding," IEEE Trans. on Circuits and Systems, Vol. 34, pp. 1306-1336, 1987.
  6. M. Bomans, K. Hohne, U. Tiede and M. Riemer, "3D segmentation of MR images of the head for 3D display," IEEE Transactions on Medical imaging, Vol. 9, pp. 253-277, 1990.
  7. F. Meyer, S. Beucher, "Morphological Segmentation", Journal of Visual Communication and Image Representation, 1 , pp. 21-46, 1990.
  8. S. Beucher, M. Bilodeau, X. Yu. Road segmentation by watershed algorithms. Proceedings of the Pro-art vision group PROMETHEUS workshop, Sophia-Antipolis, France,pp pp. 299-314, April 1990.
  9. R. Harlick and L. Shapiro, "Image segmentation technique," CVGIP, Vol. 29, pp. 100-137, 1985.
  10. Vicent L. Solille P, Watershed in digital spaces, "An efficient algorithm based immersion simulations", IEEE Transections PAMI, pp. 538-598, 1991. M. Kunt, M. Bernard and R. Leonardi, "Recent results in high compression image coding," IEEE Trans. on Circuits and Systems, Vol. 34, pp. 1306-1336, 1987.
  11. F. D. Natale, G. Desoli, D. Glusto and G. Vernazza, "Polynomial approximation and vector quantization: A region based integration," IEEE transections on Communications, Vol. 43, 1995.
  12. K. Haris, "A hybrid algorithm for the segmentation of 2D and 3D images," Master's thesis, University of Crete, 1994.
  13. R. Harlick and L. Shapiro, "Image segmentation technique," CVGIP, Vol. 29, pp. 100-137, 1985.
  14. S. Beucher, "Watershed, hierarchical segmentation and water fall algorithm," in Mathematical Morphology and Its Applications to Image Processing, Dordrecht, The Netherlands: Kluwer, 1994, pp. 69–76.
  15. Beucher, S. , and Meyer, F. The morphological approach to segmentation: the watershed transformation. In Mathematical Morphology in Image Processing, E. R. Dougherty, Ed. Marcel Dekker, New York, ch. 12, pp. 433-481, 1993.
  16. F. Meyer, S. Beucher, "Morphological Segmentation," Journal of Visual Communication and Image Representation,vol. 1, pp. 21-46, 1990.
  17. Gonzalez & Woods, Digital Image Processing, 3rd edition, Prentice Hall India, 2008.
  18. K. Haris,"Hybrid image segmentation using watersheds and fast region merging," IEEE Trans Image Processing, 7(12), pp. 1684-1699, 1998.
  19. S. Thilagamani, N. Shanthi, "A Novel Recursive Clustering Algorithm for Image Oversegmentation", European Journal of Scientific Research, Vol. 52, No. 3, pp. 430-436, 2011.
  20. Peter Eggleston, "Understanding Oversegmentation and Region Merging", Vision Systems Design, December 1, 1998.
  21. S. Beucher, "The Watershed Transformation Applied To Image Segmentation", Centre De Morphologie Mathématique. Ecole Des Mines De Paris, 1991.
  22. Shameem Akthar, D. RajyaLakshmi , Syed Abdul Sattar, "Suppression of Over Segmentation in Watershed Transform Using Pre Processing Method", International Journal of Computer Technology and Electronics Engineering, Volume 2, Issue 2, April 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Over segmentation Image smoothing Watersheds