Automated Classification of Mitotic Phenotypes of Human Cells Using Fluorescent Proteins

Methods in Cell Biology

Volumn 85, Issue , 2008, Pages 539-554

  Harder, N ^a   Eils, R ^a   Rohr, K ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

PHOTOPROTEIN;

ALGORITHM; APOPTOSIS; AUTOMATED PATTERN RECOGNITION; CELL NUCLEUS; COMPUTER ASSISTED DIAGNOSIS; FLUORESCENCE MICROSCOPY; HELA CELL; HUMAN; IMAGE PROCESSING; METHODOLOGY; MITOSIS; PHENOTYPE; PHYSIOLOGY; REVIEW; ULTRASTRUCTURE;

ALGORITHMS; APOPTOSIS; CELL NUCLEUS; HELA CELLS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; LUMINESCENT PROTEINS; MICROSCOPY, FLUORESCENCE; MITOSIS; PATTERN RECOGNITION, AUTOMATED; PHENOTYPE;

EID: 37249078756     PISSN: 0091679X     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0091-679X(08)85023-6     Document Type: Review

References (25)

1. Boland M.V., Markey M., and Murphy R.F. Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images. Cytometry 33 (1998) 366-375
2. Boland M.V., and Murphy R.F. A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17 12 (2001) 1213-1223
3. Burkhardt H., and Siggelkow S. "Invariant Features in Pattern Recognition-Fundamentals and Applications." (2001), John Wiley & Sons, New York.
4. Chang C.-C., and Lin C.-J. "LIBSVM: A Library for Support Vector Machines." (2001). http://www.csie.ntu.edu.tw/cjlin/libsvm Software available at
5. Chang T., and Kuo C.-C.J. Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2 4 (1993) 429-441
6. Conrad C., Erfle H., Warnat P., Daigle N., Lörch T., Ellenberg J., Pepperkok R., and Eils R. Automatic identification of subcellular phenotypes on human cell arrays. Genome Res. 14 (2004) 1130-1136
7. Danckaert A., Gonzalez-Couto E., Bollondi L., Thompson N., and Hayes B. Automated recognition of intracellular organelles in confocal microscope images. Traffic 3 (2002) 66-73
8. Friedman A., and Perriman N. Genome-wide high-throughput screens in functional genomics. Curr. Opin. Genet. Dev. 14 (2004) 470-476
9. Gallardo G., Yang F., Ianzini F., Mackey M., and Sonka M. Mitotic cell recognition with hidden Markov models. In: Galloway J.R.L. (Ed). "Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, Proc SPIE" Vol. 5367 (2004), SPIE, Bellingham, WA 661-668
10. Gonzalez R.C., and Woods R.E. "Digital Image Processing.". 2nd edn. (2002), Prentice Hall, New Jersey, NJ
11. Haralick R.M. Statistical and structural approaches to texture. Proc IEEE 67 (1979) 786-804
12. Harder N., Neumann B., Held M., Liebel U., Erfle H., Ellenberg J., Eils R., and Rohr K. Automated recognition of mitotic patterns in fluorescence microscopy images of human cells. In: Kovačević J., and Meijering E. (Eds). "Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'06)" Arlington, VA, April 2006 (2006), IEEE, New York 1016-1019
13. Hsu C.-W., and Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13 (2002) 415-425
14. Huang K., and Murphy R.F. Automated classification of subcellular patterns in multicell images without segmentation into single cells. In: Leahy R.M., and Roux C. (Eds). "Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '04)", Arlington, VA, April 2004 (2004), IEEE, New York 1139-1142
15. Kovalev V., Harder N., Neumann B., Held M., Liebel U., Erfle H., Ellenberg J., Eils R., and Rohr K. Feature selection for evaluating fluorescence microscopy images in genome-wide cell screens. In: Schmid C., Soatto S., and Tomasi C. (Eds). "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)" New York, NY, June 2006 (2006), IEEE, New York 276-283
16. Lindblad J., Wählby C., Bengtsson E., and Zaltsman A. Image analysis for automatic segmentation of cytoplasms and classification of Rac1 activation. Cytometry A 57A (2003) 22-33
17. Neumann B., Held M., Liebel U., Erfle H., Rogers P., Pepperkok R., and Ellenberg J. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3 (2006) 385-390
18. Otsu N. A threshold selection method from grey level histograms. IEEE Trans. Syst. Man Cybern. 9 (1979) 62-66
19. Perlman Z., Slack M., Feng Y., Mitchison T., Wu L., and Altschuler S. Multidimensional drug profiling by automated microscopy. Science 306 (2004) 1194-1198
20. Prokop R.J., and Reeves A.P. A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graph. Models Image Process. 54 5 (1992) 438-460
21. Vapnik V. "The Nature of Statistical Learning Theory." (1995), Springer-Verlag, New York
22. Vapnik V. "Statistical Learning Theory." (1998), John Wiley and Sons, New York
23. Würflinger T., Stockhausen J., Meyer-Ebrecht D., and Böcking A. Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images. Comput. Med. Imaging Graph. 28 (2004) 87-98
24. Zernike F. Beugungstheorie des Schneidenverfahrens und seiner verbesserten Form, der Phasenkontrastmethode. Physika 1 (1934) 689-704
25. Zhou X., Liu K.-Y., Bradley P., Perrimon N., and Wong S.T. Towards automated cellular image segmentation for RNAi genome-wide screening. Vol. 3749 of LNCS (Palm Springs), CA. In: Duncan J., and Gering G. (Eds). "Proceedings of the Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'2005)", Oct. 2005 (2005), Springer-Verlag, Berlin, Heidelberg 302-309