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Comparing marker definition algorithms for watershed segmentation in microscopy images
Keywords:
Segmentation, Digital Image processing, Fuzzy logic, clusteringAbstract
Segmentation is often a critical step in image analysis. Microscope image components show great variability of shapes, sizes, intensities and textures. An inaccurate segmentation conditions the ulterior quantification and parameter measurement. The Watershed Transform is able to distinguish extremely complex objects and is easily adaptable to various kinds of images. The success of the Watershed Transform depends essentially on the existence of unequivocal markers for each of the objects of interest. The standard methods of marker detection are highly specific, they have a high computational cost and they determine markers in an effective but not automatic way when processing highly textured images. This paper compares two different pattern recognition techniques proposed for the automatic detection of markers that allow the application of the Watershed Transform to biomedical images acquired via a microscope. The results allow us to conclude that the method based on clustering is an effective tool for the application of the Watershed Transform.
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References
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[17] M. Ceccarelli, F. Musacchia and A. Petrosino, A fuzzy scale-space approach to feature-based image representation and retrieval. Brain Vision and Artificial Intelligence 2006, 377-385
[18] Ceccarelli M and Petrosino A, A paralel fuzzy scale-space approach to the unsupervised texture separation. Pattern Recognition Letters 2002, 5: 557-567
[19] Cheng H D, Chen Y H and Sun Y, A vel fuzzy entropy aproach to image enhancement and thresholding. Signal Processing 1999, 75:277-301
[20] Gonzalez M A and Ballarin V L,Segmentación de imágenes mediante la Transformada Watershed: Obtención de marcadores mediante Morfología Matemática. 9vo Simposio Argentino de Informática y Salud (SIS), 2006
[21] S. E. Hernandez and K. E. Barner, Joint region merging criteria for Watershed-Based image segmentation. IEEE Trans on Image Processing, 2000
[22] J. B. Mc Queen, Some Methods for Classification and Analysis of Multivariate Observation. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability 1967, Berkeley University of California Press, 281-297.
[23] J.Pastore,E.Moler andV.Ballarin, Comparison of segmentation techniques by means of non-euclidean distances.Digital Signal Processing (revisión) 2006.
[2] L Vincent and P. Soille, Watersheds in digital spaces : An efficient algorithm based on immersion simulations, IEEE trans. Pattern Anal, Machine Intell 1991, 13:583-598
[3] J. M. Gauch, Image segmentation and analysis via multiescale gradient watershed hierarchies. IEEE Trans. on Image Processing 1999, 8
[4] P. Jackway, “Gradient Watersheds in morphological scale-space, IEEE trans. Image Processing, Machine Intell. 1996, 5:913-921
[5] D. Wang, A multiscale gradient algorithm for Image Segmentation using Watersheds. Pattern Reconition 1997, 30:2043-2052
[6] P. V. Gorsevski, P. E. Gessler and P. Jankowski , Integrating a fuzzy k-means classification and bayesian approach for spatial prediction of landslide hazard. J. of Geographical Systems 2003, 5:5930-5949
[7] X. Li and G. Harnarneh, Modeling prior shape and appearance knowledge in watershed segmentation. Image Vision Comput 2007
[8] M. A. Gonzalez and V. L. Ballarin, Determinación automática de marcadores para la Transformada Watershed mediante técnicas de clasificación de patrones, 9vo Simposio Argentino de Informática y Salud (SIS), 2006
[9] SDC Morphology Toolbox for MATLAB 5. User’s Guide. SDC Information Systems, 2001.
[10] K.Castleman, Digital Image Processing. Ch. 15, Prentice Hall 1979
[11] R. González and R. Woods, Tratamiento Digital de imágenes. Addison Wesley 1996
[12] S. Beucher and F. Meyer, The morphological approach to segmentation: The Watershed Transformation, Mathematical Morphology and its application to image processing, optical engineering. TT16 1994
[13] M. Couprie and G. Bertrant, Topological grayscale Watershed Transform. SPIE Vision Geometry Proceedings 1997, 3168:136-146
[14] E. R. Dougherty J. and Astola, An introduction to nonlinear image processing. Ed. New York: Marcel Decker 1993, 433-481
[15] J. Facon, Morfología Matemática. Teoría y ejemplos. Curitiba Brasil, CITS
[16] L. S .Stanislav and S. Wolfgang, Extracting regions of interest appying a local watershed transformation. Proc. of IEEE Visualization 2000
[17] M. Ceccarelli, F. Musacchia and A. Petrosino, A fuzzy scale-space approach to feature-based image representation and retrieval. Brain Vision and Artificial Intelligence 2006, 377-385
[18] Ceccarelli M and Petrosino A, A paralel fuzzy scale-space approach to the unsupervised texture separation. Pattern Recognition Letters 2002, 5: 557-567
[19] Cheng H D, Chen Y H and Sun Y, A vel fuzzy entropy aproach to image enhancement and thresholding. Signal Processing 1999, 75:277-301
[20] Gonzalez M A and Ballarin V L,Segmentación de imágenes mediante la Transformada Watershed: Obtención de marcadores mediante Morfología Matemática. 9vo Simposio Argentino de Informática y Salud (SIS), 2006
[21] S. E. Hernandez and K. E. Barner, Joint region merging criteria for Watershed-Based image segmentation. IEEE Trans on Image Processing, 2000
[22] J. B. Mc Queen, Some Methods for Classification and Analysis of Multivariate Observation. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability 1967, Berkeley University of California Press, 281-297.
[23] J.Pastore,E.Moler andV.Ballarin, Comparison of segmentation techniques by means of non-euclidean distances.Digital Signal Processing (revisión) 2006.
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Published
2008-10-01
How to Cite
González, M. A., Cuadrado, T. R., & Ballarín, V. L. (2008). Comparing marker definition algorithms for watershed segmentation in microscopy images. Journal of Computer Science and Technology, 8(03), p. 151–157. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/757
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Original Articles
