Counting cells in 3D confocal images based on discriminative models

2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011

Volumn , Issue , 2011, Pages 399-403

  Zhou, Jie ^a   Peng, Hanchuan ^b  

^a NORTHERN ILLINOIS UNIVERSITY   (United States)

^b HOWARD HUGHES MEDICAL INSTITUTE   (United States)

Author keywords

3D cell counting; Classification; Discriminative model; High content cellular image quantification

Indexed keywords

BINARY CLASSIFICATION; CELL COUNTING; CELLULAR IMAGE; COMPUTATIONALLY EFFICIENT; CONFOCAL IMAGE; DISCRIMINATIVE MODELS; DISEASE DIAGNOSIS; DROSOPHILA MELANOGASTER; FRUIT FLIES; HIGH-CONTENT; HIGH-THROUGHPUT; TEXTURE FEATURES; UNSUPERVISED APPROACHES;

BIOINFORMATICS; CELLS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; CYTOLOGY; IMAGE PROCESSING; PATTERN RECOGNITION; TEMPLATE MATCHING; THREE DIMENSIONAL COMPUTER GRAPHICS;

THREE DIMENSIONAL;

EID: 84858974831     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2147805.2147858     Document Type: Conference Paper

References (15)

1. Gil J., Wu H., and Wang B.Y. Image analysis and morphometry in the diagnosis of breast cancer. Microsc Res Tech, 59:109-118, 2002. (Pubitemid 35154913)
2. Carpenter A. E., et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes, Genome Biology, 7:R100, 2006.
3. Fuller C. J. and Straight A. F., Image Analysis Benchmarking Methods for High-Content Screen Design, Journal of Microscopy, vol.238, Pt2, pp145-161. 2010
4. Peng, H. C., Bioimage Informatics: A new area of engineering biology. Bioinformatics, vol 24, No.17, pp1827-1836, 2008.
5. Zhou, J. and Peng H.C. Automatic recognition and annotation of gene expressions of Fly Embryos, Bioinformatics, 23(5):589-596, 2007. (Pubitemid 46522592)
6. Peng H. C., Long, F. H., Zhou J., Leung G, Eisen M., and Myers E, Automatic image analysis for gene expression patterns of fly embryos. BMC Cell Biology. Vol. 8, No. Supp.1, pp.S7, 2007.
7. Held M., et al. CellCognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nature Methods 7, 747-754, 2010.
8. Shamir, L., Orlov, N., Eckley, D.E., Macura, T., Johnston, J., Goldberg, I., Wndchrm - an open source utility for biological image analysis, BMC Source Code for Biology and Medicine, 3: 13, 2008.
9. Walter T. et al, Visualization of image data from cells to organisms. Nature Methods. 7, S26-S41. 1 March 2010.
10. Peng H., Ruan, Z, Long F, Simpson J. H. & Myers E. W., V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets, Nature Biotechnology, Vol. 28, No. 4, pp. 348-353, 2010.
11. Fiji: http://pacific.mpi-cbg.de/wiki/index.php/Fiji
12. Chen X., Velliste M., Weinstein S., Jarvik J. W., and Murphy R.F. Location proteomics - Building subcellular location trees from high resolution 3D fluoresence microscope images of randomly-tagged proteins. Proc. SPIE 4962: 298-306 2003.
13. Velliste M. and Murphy R. F, Automated determination of protein subcellular locations from 3D fluorescence microscope images, Proceedings of the 2002 IEEE International Symposium on Biomedical Imaging, pp. 867-870.
14. Bjornsson C. S. et al. Associative image analysis: A method for automated quantification of 3D multi-parameter images of brain tissue. J. of Neuroscience Methods 170: 165-178. 2008.
15. Aponte, Y. et al. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training, Nature Neuroscience, 2011, doi:10.1038/nn.2739