Segmentation of touching cell nuclei using a two-stage graph cut model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5575 LNCS, Issue , 2009, Pages 410-419

  Daněk, Ondřej ^a   Matula, Pavel ^a   Ortiz De Solórzano, Carlos ^b   Muñoz Barrutia, Arrate ^b   Maška, Martin ^a   Kozubek, Michal ^a  

^a MASARYK UNIVERSITY   (Czech Republic)

^b UNIVERSITY OF NAVARRA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

APRIORI; COMBINATORIAL GRAPHS; COMPUTATIONAL DEMANDS; FLUORESCENCE MICROSCOPY IMAGES; HIGH PROBABILITY; IMAGE GRADIENTS; ISOLATED CELLS; MARKOV RANDOM FIELDS; MAXIMUM A POSTERIORI ESTIMATION; SINGLE USERS; TOUCHING CELLS; TWO STAGE;

COMBINATORIAL MATHEMATICS; FLUORESCENCE MICROSCOPY; HIDDEN MARKOV MODELS; IMAGE ANALYSIS; IMAGE SEGMENTATION;

BIOINFORMATICS;

EID: 70350662250     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02230-2_42     Document Type: Conference Paper

References (13)

1. Pratt, W.K.: Digital Image Processing. Wiley, Chichester (1991)
2. Soille, P.: Morphological Image Analysis, 2nd edn. Springer, Heidelberg (2004)
3. Ortiz de Solórzano, C., Malladi, R., Leliévre, S.A., Lockett, S.J.: Segmentation of nuclei and cells using membrane related protein markers. Journal of Microscopy 201, 404-415 (2001)
4. Malpica, N., Ortiz de Solórzano, C., Vaquero, J.J., Santos, A., Lockett, S.J., Vallcorba, I., García-Sagredo, J.M., Pozo, F.d.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28, 289-297 (1997)
5. Nilsson, B., Heyden, A.: Segmentation of dense leukocyte clusters. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis, pp. 221-227 (2001)
6. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: IEEE International Conference on Computer Vision, July 2001, vol. 1, pp. 105-112 (2001)
7. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. International Journal of Computer Vision 70(2), 109-131 (2006)
8. Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 147-159 (2004)
9. Boykov, Y., Veksler, O., Zabih, R.: Markov random fields with efficient approximations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 648-655. IEEE Computer Society, Los Alamitos (1998)
10. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222-1239 (2001)
11. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124-1137 (2004)
12. Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: IEEE International Conference on Computer Vision, vol. 1, pp. 26-33 (2003)
13. Dahlhaus, E., Johnson, D.S., Papadimitriou, C.H., Seymour, P.D., Yannakakis, M.: The complexity of multiterminal cuts. SIAM J. Comput. 23(4), 864-894 (1994)