Improved Watershed Algorithm for Cell Image Segmentation

Article Preview

Abstract:

Watershed is an image segmentation algorithm based on mathematical morphology, which can determine the boundary of connected section efficiently and effectively. But the traditional watershed algorithm is sensitive to noise. To overcome the weakness of classical watershed, this paper presents an improved watershed algorithm based on gradient transform, open-close reconstruction and distance transform. The experiment result shows that application of this improved watershed algorithm in cell image segmentation has a good performance.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 546-547)

Pages:

464-468

Citation:

Online since:

July 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Luc Vincent and Pierre soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, Vol. 13, No. 6, pp.583-598.

DOI: 10.1109/34.87344

Google Scholar

[2] Huang Qi-kun and Zhou Yun-cai, A method based on watershed algorithm for core particles image segmentation, Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, 2010, vol. 3, pp.408-410.

DOI: 10.1109/iccsit.2010.5564001

Google Scholar

[3] Yuqian Zhao, Jianxin Liu, Huifen Li and Guiyuan Li, Improved watershed algorithm for dowels image segmentation, Proceedings of the 7th world congress on intelligent control and automation, June 25-27, 2008, Chongqing, China, pp.7644-7648.

DOI: 10.1109/wcica.2008.4594115

Google Scholar

[4] Yu Zhang and Duanquan Xu, Improvement on Watershed algorithm of OpenCV and its application in cell image segmentation, unpublished.

Google Scholar

[5] Vincent, L., Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms, IEEE Transactions on Image Processing, Vol. 2, No. 2, April, 1993, pp.176-201.

DOI: 10.1109/83.217222

Google Scholar

[6] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd ed, Prentice Hall, 2002, pp.134-137.

Google Scholar

[7] Otsu, N., A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp.62-66.

DOI: 10.1109/tsmc.1979.4310076

Google Scholar

[8] Maurer, Calvin, Rensheng Qi, and Vijay Raghavan, A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 2, February 2003, pp.265-270.

DOI: 10.1109/tpami.2003.1177156

Google Scholar

[9] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 2nd ed, Prentice Hall, 2002, pp.123-124.

Google Scholar

[10] Meyer, Fernand, Topographic distance and watershed lines, Signal Processing , Vol. 38, July 1994, pp.113-125.

DOI: 10.1016/0165-1684(94)90060-4

Google Scholar

[11] Soille, P., Morphological Image Analysis: Principles and Applications, Springer-Verlag, 1999, pp.173-174.

Google Scholar