We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Watershed Segmentation based on Distance Transform and Edge Detection Techniques

by Pinaki Pratim Acharjya, Dibyendu Ghoshal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 13
Year of Publication: 2012
Authors: Pinaki Pratim Acharjya, Dibyendu Ghoshal
10.5120/8259-1792

Pinaki Pratim Acharjya, Dibyendu Ghoshal . Watershed Segmentation based on Distance Transform and Edge Detection Techniques. International Journal of Computer Applications. 52, 13 ( August 2012), 6-10. DOI=10.5120/8259-1792

@article{ 10.5120/8259-1792,
author = { Pinaki Pratim Acharjya, Dibyendu Ghoshal },
title = { Watershed Segmentation based on Distance Transform and Edge Detection Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 13 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number13/8259-1792/ },
doi = { 10.5120/8259-1792 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:07.202804+05:30
%A Pinaki Pratim Acharjya
%A Dibyendu Ghoshal
%T Watershed Segmentation based on Distance Transform and Edge Detection Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 13
%P 6-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An edge detection algorithm for digital images is proposed in this paper. Edge detection is one of the important and most difficult tasks in image processing and analysis. In images edges can create major variation in the picture quality where edges are areas with strong intensity contrasts. Edges in digital images are areas with strong intensity contrasts and a jump in intensity from one pixel to the next can create major variation in the picture quality. This paper proposed an effective edge detection algorithm based morphological edge detectors and watershed segmentation algorithm using distance transform. The result confirms that the proposed algorithm is found to yield satisfactory and efficient segmentation of the digital images for edge detection. Experimental result presented in this paper is obtained by using MATLAB.

References
  1. P. Suetnes, P. Fua and A. J. Hanson, "Computational strategies for object recognition," ACM Computing Surveys, Vol. 24, pp. 05-61, 1992.
  2. R. Besl, R. Jain, "Three dimensional object recognition," ACM Computing Surveys, Vol. 17, pp. 75-145, 1985.
  3. K. Hohne, H. Fuchs, S. Pizer, "3D imaging in medicine: Algorithms, systems, Applications", Berlin, Germany, Springer –Verlag, 1990.
  4. 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.
  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. P. Willemin, T. Reed and M. Kunt, "Image sequence coding by split and merge," IEEE Trans. on Circuits and Systems, Vol. 34, pp. 1306-1306, 1987.
  7. 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.
  8. K. Haris, "A hybrid algorithm for the segmentation of 2D and 3D images," Master's thesis, University of Crete, 1994.
  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.
  11. Gonzalez & Woods, Digital Image Processing, 3rd edition, Prentice Hall India, 2008.
  12. K. Haris,"Hybrid image segmentation using watersheds and fast region merging," IEEE Trans Image Processing, 7(12), pp. 1684-1699, 1998.
  13. Jos B. T. M. Roerdink and Arnold Meijster. : "The watershed transform: Definitions, algorithms and parallelization strategies," Fundamental Informatics, Vol. 41, pp. 187-228, 2001.
  14. Hua LI et al. , "An improved image segmentation approach based on level set and mathematical morphology," GREYC-ISMRA, CNRS 6072, 6 Bd Maréchal Juin, 14050 Caen, France.
  15. Mahua Bhattacharya, Arpita Das, "A Study on Seeded Region Based Improved Watershed Transformation for Brain Tumor segmentation," Indian Institute of Information Technology & Management, Gwalior Morena Link Road, Gwalior-474010.
  16. D. Marr and E. Hildreth, "A theory of edge detection," Proc. R. Soc. London, no 207, pp. 187-217.
  17. Chen Pan, Congxun Zheng, Hao-Jun Wang "Robust Color Image Segmentation Based On Mean Shift And Marker-controlled Watershed Algorithm", Second International Conference on Machine Learning and Cybernetics, Wan, ,pp 2752-2756, 2003.
  18. F. Meyer, S. Beucher, "Morphological Segmentation", Journal of Visual Communication and Image Representation, 1, pp. 21-46, 1990.
  19. N. Pal and S. pal, "A review on image segmentation techniques," Pattern Recognition, Vol. 26, pp. 1277-1294, 1994.
  20. K. Haris, G. Tziritas and S. Orphanoudakis, "Smoothing 2D or 3D images using local classification," in Proc. EUSIPCO, Edinburg, U. K. , 19994.
Index Terms

Computer Science
Information Sciences

Keywords

Edge detection Segmentation Distance Transform Watersheds