Unsupervised Cell Nuclei Segmentation Based on Morphology and Adaptive Active Contour Modelling

1. Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survay of thresholding techniques. Computer Vision, Graphics, and Image Processing 41(2), 233–260 (1988)

   Article  Google Scholar 

2. Lee, K.M., Street, W.N.: An adaptive resource-allocating network for automatic detection, segmentation, and classification of breast cancer nuclei topic area: image processing and recognition. IEEE Transaction on Neural Network 14(3), 680–687 (2003)

   Article  Google Scholar 

3. Ruberto, C.D., Dempster, A., Kan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image and Vision Computing 20, 133–146 (2002)

   Article  Google Scholar 

4. Hu, M., Ping, X., Ding, Y.: Automated cell nucleus segmentation using improved snake. In: Proceedings of the International Conference on Image Processing, pp. 2737–2740 (2004)

   Google Scholar 

5. Gurcan, M.N., Pan, T., Shimada, H., Saltz, J.: Image analysis for neuroblastoma classification: segmentation of cell nuclei. In: Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 4844–4847 (2006)

   Google Scholar 

6. Yang, F., Mackey, M.A., Ianzini, F., Gallardo, G., Sonka, M.: Cell segmentation, tracking, and mitosis detection using temporal context. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 302–309. Springer, Heidelberg (2005)

   Chapter  Google Scholar 

7. Zhang, B., Zimmer, C., Olivo, M.J.C.: Tracking fluorescent cells with coupled geometric active contours. In: Proceedings of the IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 476–479 (2004)

   Google Scholar 

8. Yang, L., Meer, P., Foran, D.J.: Unsupervised segmentation based on robust estimation and color active contour models. IEEE Transactions on Information Technology in Biomedicine 9(3), 475–486 (2005)

   Article  Google Scholar 

9. Li, C., Kao, C., John, C., Ding, Z.: Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transactions on Image Processing 17(10), 1940–1949 (2008)

   Article  MathSciNet  Google Scholar 

10. Yang, Y., Li, C., Kao, C.-Y., Osher, S.: Split bregman method for minimization of region-scalable fitting energy for image segmentation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammound, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010, Part II. LNCS, vol. 6454, pp. 117–128. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

11. Goldstein, T., Bresson, X., Osher, S.: Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction. Journal of Scientific Computing 45, 272–293 (2010)

    Article  MathSciNet  MATH  Google Scholar 

12. Haralick, R., Sternberg, S., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532–550 (1987)

    Article  Google Scholar 

13. Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of denoising and segmentation models. SIAM Journal on Applied Mathematics 66, 1632–1648 (2006)

    Article  MathSciNet  MATH  Google Scholar 

14. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J., Osher, S.: Fast Global Minimization of the Active Contour/Snake Model. Journal of Mathematical Imaging and Vision 28, 151–167 (2007)

    Article  MathSciNet  Google Scholar 

15. Robb, R.: Biomedical imaging, visualization, and analysis. Wiley-Liss, USA (2000)

    Google Scholar 

16. Adobe photoshop, http://en.wikipedia.org/wiki/Adobe_Photoshop

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