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

Segmentation of Abnormal Region from Endoscopic Images using Intelligent Scissors

Print
PDF
RTIPPR
© 2010 by IJCA Journal
Number 2 - Article 4
Year of Publication: 2010
Authors:
Ravindra S. Hegadi
Shailaja S. Halli
Arpana Kop

Ravindra S Hegadi, Shailaja S Halli and Arpana Kop. Segmentation of Abnormal Region from Endoscopic Images using Intelligent Scissors. IJCA,Special Issue on RTIPPR (2):89–96, 2010. Published By Foundation of Computer Science. BibTeX

@article{key:article,
	author = {Ravindra S. Hegadi and Shailaja S. Halli and Arpana Kop},
	title = {Segmentation of Abnormal Region from Endoscopic Images using Intelligent Scissors},
	journal = {IJCA,Special Issue on RTIPPR},
	year = {2010},
	number = {2},
	pages = {89--96},
	note = {Published By Foundation of Computer Science}
}

Abstract

The commonly found abnormalities in endoscopic images are cancer tumors, ulcers, bleeding due to internal injuries, etc. Several methods of segmentation are employed in recent past for proper segmentation of such images. Intelligent scissors is one of the tools for segmentation. Here the segmentation of endoscopic images is presented using the intelligent scissors. This method is used to segment the tumor, abnormal regions and cancerous growth in the human esophagus. Several methods implemented in the recent past have yielded good results. But this method is simpler. Here a seed point is selected then the cost matrix is constructed which gives the costs of the neighbouring points. Using Dijkstra’s algorithm, the nearest point falling in the same region is selected. The proposed method has shown encouraging results in segmenting the abnormal parts from esophegal endoscopic images.

Reference

  • E. W. Dijkstra, “A Note on Two Problems in Connexion with Graphs,” Numerische Mathematik,Vol. 1, pp. 269-270, 1959.
  • D. H. Ballard and J. Sklansky, “Tumor Detection in Radiographs”, Computers and Biomedical Research, Vol. 6, No. 4, pp. 299-321, Aug. 1973
  • J. D. Cappelletti and A. Rosenfeld, “Three-Dimensional Boundary Following,” Computer Vision, Graphics, and Image Processing, Vol. 48, No. 1, pp. 80-92, Oct. 1989.
  • Y. P. Chien and K. S. Fu, “A Decision Function Method for Boundary Detection” Computer Graphics and Image Processing, Vol. 3, No. 2, pp. 125-140, June 1974.
  • A. Martelli, “An Application of Heuristic Search Methods to Edge and Contour Detection,” Communications of the ACM, Vol. 19, No. 2, pp. 73-83, Feb. 1976.
  • B. S. Morse, W. A. Barrett, J. K. Udupa, and R. P. Burton, Trainable Optimal Boundary Finding Using Two-Dimensional Dynamic Programming. Technical Report No. MIPG180, Department of Radiology, University of Pennsylvania, Philadelphia, PA, March 1991.
  • U. Montanari, “On the Optimal Detection of Curves in Noisy Pictures,” Communication of the ACM, Vol. 14, No. 5, pp. 335-345, May 1971.
  • D. H. Ballard and J. Sklansky, “Tumor Detection in Radiographs”, Computers and Biomedical Research, Vol. 6, No. 4, pp. 299-321, Aug. 1973
  • Y. P. Chien and K. S. Fu, “A Decision Function Method for Boundary Detection” Computer Graphics and Image Processing, Vol. 3, No. 2, pp. 125-140, June 1974.
  • A. Martelli, “An Application of Heuristic Search Methods to Edge and Contour Detection,” Communications of the ACM, Vol. 19, No. 2, pp. 73-83, Feb. 1976.
  • N. J. Nilsson, Principles of Artificial Intelligence. Palo Alto, CA: Tioga, 1980.
  • J. K. Udupa, Personal communication to W. A. Barrett regarding two-dimensional boundary detection using dynamic programming with graph searching. 1989.
  • B. S. Morse, Trainable Automated Boundary Tracking Using Two-Dimensional Graph Searching with Dynamic Programming. Master’s Thesis, Department of Computer Science, Brigham Young University, Provo, UT, Aug. 1990.
  • B. S. Morse, W. A. Barrett, J. K. Udupa, and R. P. Burton, Trainable Optimal Boundary Finding Using Two-Dimensional Dynamic Programming. Technical Report No. MIPG180, Department of Radiology, University of Pennsylvania, Philadelphia, PA, March 1991.
  • A. A. Amini, T. E. Weymouth, and R. C. Jain, “Using Dynamic Programming for Solving Variational Problems in Vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol. 12, No. 9, pp. 855-866, Sept. 1990.
  • L. D. Cohen and R. Kimmel, “Global Minimum for Active Contour Models: A Minimum Path Approach,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ‘96), San Francisco, CA, June 1996.
  • D. Daneels, et al., “Interactive Outlining: An Improved Approach Using Active Contours,” in SPIE Proceedings Storage and Retrieval for Image and Video Databases, Vol. 1908, pp. 226-233, San Jose, CA, Feb. 1993.
  • D. Geiger, A. Gupta, L. A. Costa, and J. Vlontzos, “Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 3, pp. 294-302, Mar. 1995 (Correction in PAMI, Vol. 18, No. 5, pg. 575, May 1996)
  • M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” in Proceedings of the First International Conference on Computer Vision, pp. 259-268, London, England, June 1987.
  • M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” International Journal of Computer Vision, Vol. 1, No. 4, pp. 321-331, Jan. 1988.
  • D. J. Williams and M. Shah, “A Fast Algorithm for Active Contours and Curvature Estimation,” CVGIP: Image Understanding, Vol. 55, No. 1, pp. 14-26, Jan. 1992
  • A. X. Falcão, J. K. Udupa, S. Samarasekera, and B. E. Hirsch, “User-Steered Image Boundary Segmentation,” in Proceedings of the SPIE--Medical Imaging 1996: Image Processing, Vol. 2710, pp. 278-288, Newport Beach, CA, Feb. 1996.
  • J. K. Udupa, S. Samarasekera, and W. A. Barrett, “Boundary Detection via Dynamic Programming,” in Proceedings of the
  • SPIE: Visualization in Biomedical Computing 92, Vol. 1808, pp. 33-39, Chapel Hill, NC, Oct. 1992.
  • W. A. Barrett, P. D. Clayton, and H. R. Warner, “Determination of Left Vetricular Contours: A Probabilistic Algorithm Derived from Angiographic Images,” Computers and Biomedical Research, Vol. 13, No. 6, pp. 522-548, Dec. 1980.
  • M. M. Fleck, “Multiple Widths Yield Reliable Finite Differences,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 4, pp. 412-429, April 1992.
  • H. Jeong and C. I. Kim, “Adaptive Determination of Filter Scales for Edge Detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 5, pp. 579-585, May 1992.
  • D. Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of the Royal Society of London--Series B: Biological Sciences, Vol. 207, No. 1167, pp. 187-217, Feb. 29, 1980.