Email this article 
Open Access
Subscription or Fee Access
Region-based Segmentation of Dead Cancer Cells in Microscopic Cytological Image – A Reciprocal study
Abstract
In this paper the region-based image segmentation methods for segmenting dead cancer cells in microscopic cytological image of Swiss albino mice are presented. We have used three region-based segmentation methods: Thresholding based morphology; Region growing and distance transform based watershed transform. The morphological operators and watershed transform segment the dead cancer cells with modifications in the size and shape of the original cancer cells whereas the region growing method extracts the dead cancer cells with sharp edges and retains the useful information. The segmentation time is very low in watershed transform compared with other two methods.
Keywords
References
S. Latha, R. Santhi, and S. Annapoorani, “Ant oxidative role of Cynodon Dactylon and Momordica Charantia against ELA induced Mice”, GEOBIOS, vol. 35(1), 2008, pp.13-16.
M. Hrebien, P. Stec, T. Nieczkowski, and A. Obuchowicz, “Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images”, International Journal of Applied Mathematics and Computer Science, vol. 18(2), 2008, pp. 159-170.
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed., Pearson Education, Inc., 2002.
L. B. Dorini, R. Minetto, and N. J. Leite, “White blood cell segmentation using morphological operators and scale-space analysis”, Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing, 2007, pp. 294 – 304.
S. Colantonio, Igor B. Gurevich, and O. Salvetti, “Automatic fuzzy-neural based segmentation of microscopic cell images”, International Journal of Signal and Imaging Systems Engineering, Vol. 1, No. 1, 2008, pp. 18-24.
M. E. Plissiti, A. Charchanti, O. Krikoni, and D. I. Fotiadis, “Automated segmentation of cell nuclei in PAP smear images”, Proceedings of IEEE International Special Topic Conference on Information Technology in Biomedicine, Greece, Oct. 26-28, 2006.
C. Duanggate, B. Uyyanonvara, and T. Koanantakul, “A Review of Image Analysis and Pattern Classification Techniques for Automatic PAP Smear Screening Process”, International Conference on Embedded Systems and Intelligent Technology, Thailand, 2008, pp. 212-217.
M. E Adawy, Z. Shehab, H. Keshk, and M. El Shourbagy, “A Fast Algorithm for Segmentation of Microscopic Cell Images”, International conference on Information & Communication Technology, 2006.
J. Dalle, W. K. Leow, D. Racoceanu, A. E. Tutac, and T. C. Putti, “Automatic Breast Cancer Grading of Histopathological Images”,International conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 3052-3055.
R. C. Gonzalez, R. E. Woods, and S.L.Eddins, Digital Image Processing Using MATLAB, 1st ed., Pearson Education, 2006.
C. Wählby, P. Karlsson, S. Henriksson, C. Larsson, M. Nilsson, and E. Bengtsson,, ‘Finding cells, finding molecules, finding patterns’, International Journal of Signal and Imaging Systems Engineering, vol.1(1), 2008, pp.11–17.
K. Nallaperumal, K. Krishnaveni , J. Varghese, S. Saudia , S. Annam, and P. Kumar, “A novel Multi-scale Morphological Watershed Segmentation Algorithm”, International Journal of Imaging Science and Engineering,GA, USA, vol. 1(2), 2007, pp. 60-64.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.