An improved diffusion driven watershed algorithm for image segmentation of cells

• Autorzy: Tarnawski W. , Kurzyński M.
• Języki publikacji: EN

Abstrakty

EN

The image segmentation is one of the most crucial steps in automated analysis of medical and biological images. The segmentation process allows for a detection of object contours. Due to specificity of imaging technique, a correct detection of cell contours is problematic because of the fuzzy and broken edges. Moreover, the cells are very often connected. The modified watershed algorithm based on the diffusion model presented in this paper has been successfully applied to segmentation of cells where the mentioned difficulties appear. The method was tested in contact endoscopy, a novel technique in the diagnosis of the larynx.

Słowa kluczowe

EN

image segmentation; nonlinear diffusion; watershed segmentation cell detection

PL

segmentacja obrazu; dyfuzja nieliniowa; segmentacja wododziałowa; wykrywanie komórek

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2007
• Tom: Vol. 11
• Strony: 213--220
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Tarnawski W.

• Chair of Systems and Computer Networks, Technical University of Wroclaw, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland

autor

Kurzyński M.

Bibliografia

• [1] ANDREA M, DIAS O, SANTOS A. Contact endoscopy during microlaryngeal surgery: a new technique for endoscopic examination of the larynx. Ann Otol Rhinol Laryngol,May; 104(5), pp. 333-339, 1995.
• [2] BEUCHER S. AND F. MEYER, The Morphological Approach to Segmentation: The Watershed Transformation, Mathematical Morphology in Image Processing, E.R. Dougherty, ed., Optical Engineering, pp. 433-482, 1993.
• [3] BEUCHER S. Watershed, hierarchical segmentation and waterfall algorithm, In: Math. Morphology and Its Appl. to ImageProcessing), pp. 69–76, 1994.
• [4] CANNY J.F., A Computational Approach to Edge Detection, Readings in Computer Vision: Issues, Problems, Principles and Paradigms, MA. Fischler and 0. Firschein, eds., pp. 184-203, 1986.
• [5] HIEU T. NGUYEN , QIANG JI, Improved watershed segmentation using water diffusion and local shape priors, Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06)
• [6] JAIN A. K., Fundamentals of Digital Image Processing, Information and Systems Sciences Series, pp. 49–75, Prentice–Hall, Englewood Cliffs, NJ, 1989.
• [7] JAHNE B. Digital Image Processing, Concepts , Algorithms and Scientific Applications, Springer Verlag Berlin Heideberg, pp. 482 – 487, 1997.
• [8] NAJMAN L. AND M. SCHMITT, Geodesic saliency of watershed contours and hierarchical segmentation, IEEE Trans. Pattern. Anal. Mach. Intell., 18(12), pp. 1163–1173, 1996.
• [9] PATRICK DE SMET, RUI LUI´S V. P. M. PIRES, DANNY DE VLEESCHAUWER, IGNACE BRUYLAND Activity driven nonlinear diffusion for color image watershed segmentation, Journal of Electronic Imaging 8(3), July, pp. 270– 278, 1999.
• [10] PERONA P., MALIK M., Scale-space and edge detection using anisotropic diffusion, IEEE Trans Pattern. Anal. Mach. Intell. 12(7), pp. 629–639, 1990.
• [11] VINCENT L. AND P.SOILLE. Watershed in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. on PAMI, 13(6), pp. 583–598, 1991.
• [12] WARDROP PJ, SIM S, MCLAREN K. Contact endoscopy of the larynx: a quantitative study. J Laryngol Otol., Jun, 114(6), pp. 437-440, 2000.
• [13] WEICKERT J. A review of nonlinear diffusion filtering. In: ScaleSpace, pp. 3–28, 1997.