Automatic fuzzy-neural based segmentation of microscopic cell images

International Journal of Signal and Imaging Systems Engineering

Volumn 1, Issue 1, 2008, Pages 18-24

  Colantonio, Sara ^a   Gurevich, Igor B ^b   Salvetti, Ovidio ^a  

^a CNR   (Italy)

^b DORODNICYN COMPUTING CENTER   (Russian Federation)

Author keywords

artificial neural networks; colour image segmentation; fuzzy clustering; microscopic cell images

Indexed keywords

EID: 77955723352     PISSN: 17480698     EISSN: 17480701     Source Type: Journal    
DOI: 10.1504/IJSISE.2008.017769     Document Type: Article

References (17)

1. Adiga, U.P.S. and Chaudhuri, B.B. (2001) ‘An efficient method based on watershed and rulebased merging for segmentation of 3-D histopathological images’, Pattern Recognition, Vol. 34, No. 7, pp.1449–1458.
2. Anoraganingrum, D. (1999) ‘Cell segmentation with median filter and mathematical morphology operation’, International Conference on Image Analysis and Processing Proceeding, Venice, Italy, 27–29 September, pp.1043–1046.
3. Bezdek, L.C. (1981) ‘Pattern recognition with fuzzy objective function algorithm’, Plenum Press, New York.
4. Churakova, Zh.V., Gurevich, I.B., Jernova, I.A., Kharazishvili, D.V., Khilkov, A.V., Nefyodov, A.V., Sheval, E.V. and Vorobjev, I.A. (2003) ‘Selection of diagnostically valuable features for morphological analysis of blood cells’, Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, Vol. 13, No. 2, pp.381–383.
5. Cootes, T.F. and Taylor, C.J. (1992) ‘Active shape models – ‘smart snakes’’, British Mach. Vision Conf. Proceedings, Springer-Verlag, Leeds, UK, 21–24 September, pp.266–275.
6. Cootes, T.F., Beeston, C., Edwards, G.J. and Taylor, C.J. (1999) ‘A unified framework for atlas matching using active appearance models’, in Kuba, A. and Samal, M. (Eds.): Information Processing in Medical Imaging, Springer-Verlag, Lecture Notes in Computer Science, Berlin, Germany, pp.322–333.
7. Di Rubeto, C., Dempster, A., Khan, S. and Jarra, B. (2000) ‘Segmentation of blood image using morphological operators’, 15th Int. Conference on Pattern Recognition, Proceedings, Barcelona, Spain, Vol. 3, 3–8 September, pp.397–400.
8. Ghebreab, S. and Smeulders, A.W.M. (2003) ‘Strings: variational deformable models of multivariate continuous boundary features’, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 11, pp.1399–1410.
9. Jaffe, E.S., Harris, N.L. and Stein, H. (2001) Pathology and Genetics of Tumors of Haematopoietic and Lymphoid Tissues, IARC Press, Lyon.
10. Karlosson, A., Strahlen, K. and Heyden, A. (2003) ‘Segmentation of histological section using snakes’, in Begun, J. and Gustavsson, T. (Eds.): LNCS 2749, 13th Scandinavian Conference, SCIA 2003 Halmstad, Sweden Proceedings, Halmstad, Sweden, 29 June-2 July, pp.595–602.
11. Lin, G., Adiga, U., Olson, K., Guzowski, J.F., Barnes, C.A. and Roysam, B. (2003) ‘A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks’, Cytometry A., Vol. 56, No. 1, pp.23–36.
12. Mouroutis, T., Roberts, S.J. and Bharath, A.A. (1998) ‘Robust cell nuclei segmentation using statistical modelling’, BioImaging, Vol. 6, pp.79–91.
13. Murashov, D. (2004) ‘Two-level method for segmentation of cytological images using active contour model’, 7th Int. Conference on Pattern Recognition and Image Analysis, PRIA-7 Proceedings, Vol. III, pp.814–817.
14. Perner, P., Perner, H. and Jänichen, S. (2006) ‘Recognition of airborne fungi spores in digital microscopic images’, Artificial Intelligence in Medicine, Vol. 36, No. 2, pp.137–157.
15. Riedmiller, M. and Braun, H. (1993) ‘A direct adaptive method for faster backpropagation learning: the RPROP algorithm’, The IEEE International Conference on Neural Networks – ICNN Proceedings, San Francisco, CA, USA, 3 March-1 April, pp.586–591.
16. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) Learning Internal Representations by Error Propagation, Parallel Distributed Processing, MIT Press, Cambridge, MA, pp.318–362.
17. Wu, H.S., Barba, J. and Gil, J. (2000) ‘Iterative thresholding for segmentation of cells from noisy images’, J. Microsc., Vol. 197, pp.296–304.