Method for segmentation of low contrast cytological images based on the active contour model

Proceedings of the Second IASTED International Multi-Conference on Automation, Control, and Information Technology - Signal and Image Processing

Volumn 2005, Issue , 2005, Pages 44-49

  Murashov, D ^a  

^a DORODNICYN COMPUTING CENTER   (Russian Federation)

Author keywords

Cell nuclei segmentation; Heat equation; Snakes

Indexed keywords

APPROXIMATION THEORY; DIGITAL FILTERS; DISEASES; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; MATHEMATICAL MODELS; PROBLEM SOLVING;

AUTOMATED DIAGNOSTICS; CELL NUCLEI SEGMENTATION; HEAT EQUATION; HEAT SOURCE;

CYTOLOGY;

EID: 33751291421     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. K. Rodenacker, and E. Bengtsson. A feature set for cytometry on digitized microscopic images, Anal Cell Pathol, 25(1), 2003, 1-36.
2. H. Borst, W. Abmayr and P. Gais, A thresholding method for automatic cell image segmentation, J. Histochem. Cytochem.,27(1), 1979, 180-187.
3. J. Kittler, J. Illingworth and J. Föglein, Threshold selection based on a simple image statistic, Comp. Vision Graph. Image Proc. 30, 1985, 125-147.
4. Malpica, N., Ortiz de Solorzano, C., Vaquera, J.J., et. al., Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry. 28, 1997, 289-297.
5. C. Wahlby, E.Bengtsson, Segmentation of Cell Nuclei in Tissue by Combining Seeded Watersheds with Gradient Information. In J. Begun and T. Gustavsson, eds. LNCS 2749, Proc. of 13 Scandinavian Conference, SCIA 2003, Halmstad, Sweeden, June/July, 2003, 408-414.
6. E.Bengtsson, C. Wahlby, J. Lindblad, Robust Sell Image Segmentation Methods, Pattern Recognition and Image Analysis. Advances in Mathematical Theory and Application, 14(2), 2004, 157-167.
7. Klemencic, A., Kovacic, S., Pemus, F., Automated segmentation of muscul fiber images using active contour models, Cytometry 32, 1998, 317-326
8. Karlsson, A., Strahlen, K., and Heyden, A. Segmentation of Histological Section Using Snakes. In J. Begun and T. Gustavsson, eds. LNCS 2749, Proc. of 13 Scandinavian Conference, SCIA 2003, Halmstad, Sweede, June/July 2003, 595-602.
9. Ortiz de Solorzano, C., Malladi, R., Lelievre, S.A., Lockett,S.J., Segmentation of nuclei and cells using membrane related protein markers. Journal of Microscopy, 201, 2001, 404-415.
10. Kass, M., Witkin, A., Terzopoulos, D. Snakes: Active contour models. Int. Journal on Computer Vision, 1, 1987, 321-331.
11. Xu, C., Prince, J.L. Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions On Image Processing, 7 (3), 1998, 359-369.
12. V. Caselles, F. Catte, T. Coll, F. Dibos, A geometric model for active contours. Numerische Mathematik 66,-1993, 1-31.
13. G.Sapiro. Geometric Partial Differential Equations and Image Analysis (Cambridge University Press, Cambridge, 2001).
14. Leymarie,F., Levine, M.D. Tracking Deformable Objects in the Plane Using an Active Contour Model. // IEEE Trans. Pattern Anal. Mach. Intell., 15(6), 1993, 617-634.
15. E.B. Lee, L. Markus, Foundations of optimal control theory (J. Willey & Sons, Inc., New York, London, Sydney, 1971).
16. J. J. Koederink. The structure of images. Bio. Cybern., 50, 1984, 363-370.
17. A.G. Butkovsky, Characteristics of the systems with distributed parameters (Nauka, Moscow, 1979) (In Russian).
18. P. Soille, Morphological Image Analysis: Principles and Applications (Springer-Verlag, Berlin, 2004).
19. Lindeberg, T., Scale-space Theory in Computer Vision (The Kluwer International Series in Engineering and Computer Science. Kluwer Academic Publishers, 1994).