Improved automatic detection and segmentation of cell nuclei in histopathology images

IEEE Transactions on Biomedical Engineering

Volumn 57, Issue 4, 2010, Pages 841-852

  Al Kofahi, Yousef ^a   Lassoued, Wiem ^b   Lee, William ^b   Roysam, Badrinath ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Cell nuclei; Histopathology; Image cytometry; Segmentation

Indexed keywords

BIOINFORMATICS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; ERRORS; IMAGE SEGMENTATION;

ADAPTIVE SCALE; AUTOMATED SEGMENTATION; AUTOMATIC DETECTION; AUTOMATIC SEGMENTATIONS; BINARIZATIONS; CELL NUCLEUS; DEGREE OF CLUSTERING; EDGE INFORMATION; GAUSSIAN FILTERING; GRAPH COLORINGS; HIGH CELL DENSITY; IMAGE CHARACTERISTICS; IMAGE CONTRASTS; IMAGE CYTOMETRY; IN-VITRO; IN-VIVO IMAGES; INITIAL SEGMENTATION; LAPLACIANS; MOUSE CLICKS; MULTISCALES; NEW APPLICATIONS; NOVEL METHODS; SEED POINT; SEGMENTATION ALGORITHMS; SEGMENTATION ERROR; SEGMENTATION RESULTS; SEMI-AUTOMATED;

EDGE DETECTION;

ACCURACY; ALGORITHM; ARTICLE; AUTOANALYSIS; AUTOMATION; CELL DENSITY; CELL NUCLEUS; CONTRAST; FILTRATION; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGING; SEGMENTATION; ANIMAL; AUTOMATED PATTERN RECOGNITION; BREAST; CLUSTER ANALYSIS; CYTOCHEMISTRY; CYTOLOGY; FEMALE; IMAGE CYTOMETRY; IMAGE PROCESSING; IMAGE QUALITY; MOUSE; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; TUMOR CELL LINE; ULTRASTRUCTURE;

ALGORITHMS; ANIMALS; BREAST; CELL LINE, TUMOR; CELL NUCLEUS; CLUSTER ANALYSIS; FEMALE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE CYTOMETRY; IMAGE PROCESSING, COMPUTER-ASSISTED; MICE; PATTERN RECOGNITION, AUTOMATED; PHANTOMS, IMAGING; REPRODUCIBILITY OF RESULTS;

EID: 77950245872     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2009.2035102     Document Type: Article

References (51)

1. C. Bilgin, C. Demir, C. Nagi, and B. Yener, "Cell-graph mining for breast tissue modeling and classification," in Proc. 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS 2007), pp. 531-5314.
2. C. Ortiz de Solorzano, E. Garcia Rodriguez, A. Jones, D. Pinkel, J.W. Gray, D. Sudar, and S. J. Lockett, "Segmentation of confocal microscope images of cell nuclei in thick tissue sections," J. Microsc., vol.193, no.3, pp. 212-226, Mar. 1999.
3. H. Ancin, B. Roysam, T. E. Dufresne, M. M. Chestnut, G. M. Ridder, D. H. Szarowski, and J. N. Turner, "Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy," Cytometry, vol.25, no.3, pp. 221-234, Nov. 1, 1996.
4. J. Y. Byun, M. R. Verardo, B. Sumengen, G. P. Lewis, B. S. Manjunath, and S. K. Fisher, "Automated tool for the detection of cell nuclei in digital microscopic images: Application to retinal images," Mol. Vis., vol.12, no.105-107, pp. 949-960, Aug. 16, 2006.
5. M. K. Chawla, G. Lin, K. Olson, A. Vazdarjanova, S. N. Burke, B. L. McNaughton, P. F.Worley, J. F. Guzowski, B. Roysam, and C. A. Barnes, "3D-catFISH: a system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization," J. Neurosci. Methods, vol.139, no.1, pp. 13-24, Oct. 15, 2004.
6. C. O. De Solorzano, R. Malladi, S. A. Lelievre, and S. J. Lockett, "Segmentation of nuclei and cells using membrane related protein markers," J. Microsc. Oxford, vol.201, pp. 404-415, Mar. 2001.
7. P. R. Gudla, K. Nandy, J. Collins, K. J. Meaburn, T. Misteli, and S. J. Lockett, "A high-throughput system for segmenting nuclei using multiscale techniques," Cytometry A, vol.73, no.5, pp. 451-466, May 2008.
8. G. Li, T. M. Liu, J. X. Nie, L. Guo, J. Malicki, A. Mara, S. A. Holley, W. M. Xia, and S. T. C. Wong, "Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion," Cytometry A, vol.71A, no.10, pp. 835-845, Oct. 2007.
9. G. Lin, U. Adiga, K. Olson, J. F.Guzowski, C. A. Barnes, and B. Roysam, "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, Nov. 2003.
10. G. Lin, M. K. Chawla, K. Olson, C. A. Barnes, J. F. Guzowski, C. Bjornsson, W. Shain, and B. Roysam, "A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images," Cytometry A, vol.71, no.9, pp. 724-736, Sep. 2007.
11. G. Lin, M. K. Chawla, K. Olson, J. F. Guzowski, C. A. Barnes, and B. Roysam, "Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei," Cytometry A, vol.63, no.1, pp. 20-33, 2005.
12. S. K. Nath, F. Bunyak, and K. Palaniappan, "Robust tracking of migrating cells using four-color level set segmentation," Lect. Notes Comput. Sci. (ACIVS), vol.4179, pp. 920-932, 2006.
13. S. K. Nath, K. Palaniappan, and F. Bunyak, "Cell segmentation using coupled level sets and graph-vertex coloring," in Proc. Med. Image Comput. Comput.-Assisted Intervention (MICCAI), 2006, vol.4190, pp. 101- 108.
14. B. Parvin, Q. Yang, J. Han, H. Chang, B. Rydberg, and M. H. Barcellos- Hoff, "Iterative voting for inference of structural saliency and characterization of subcellular events," IEEE Trans. Image Process., vol.16, no.3, pp. 615-623, Mar. 2007.
15. C. Russell, D. Metaxas, C. Restif, and P. Torr, "Using the Pn Potts model with learning methods to segment live cell images," in Proc. IEEE 11th Int. Conf. Comput. Vis., 2007, pp. 1-8.
16. C. Wahlby, I. M. Sintorn, F. Erlandsson, G. Borgefors, and E. Bengtsson, "Combining intensity, edge and shape information for 2D and 3D segmentation of cell nuclei in tissue sections," J. Microsc. Oxford, vol.215, pp. 67-76, Jul. 2004.
17. M.Wang, X. Zhou, F. Li, J. Huckins, R.W. King, and S. T.Wong, "Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy," Bioinformatics, vol.24, no.1, pp. 94-101, Jan. 1, 2008.
18. G. Li, T. Liu,A. Tarokh, J. Nie, L. Guo, A. Mara, S.Holley, and S. T.Wong, "3D cell nuclei segmentation based on gradient flow tracking," BMC Cell Biol., vol.8, p. 40, 2007.
19. N. Malpica, C. O. de Solorzano, J. J. Vaquero, A. Santos, I. Vallcorba, J. M. Garcia-Sagredo, and F. del Pozo, "Applying watershed algorithms to the segmentation of clustered nuclei," Cytometry, vol.28, no.4, pp. 289- 297, Aug. 1, 1997.
20. P. Soille, Morphological Image Analysis: Principles and Applications. Berlin, Germany: Springer-Verlag Telos, 1999.
21. D. H. Ballard, "Generalizing the hough transform to detect arbitrary shapes," Pattern Recognit., vol.13, no.2, pp. 111-122, 1981.
22. Q. Yang and B. Parvin, "Perceptual organization of radial symmetries," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2004, pp. 320-325.
23. H. Chang, Q. Yang, and B. Parvin, "Segmentation of heterogeneous blob objects through voting and level set formulation," Pattern Recognit. Lett., vol.28, no.13, pp. 1781-1787, Oct. 1, 2007.
24. Q. Yang and B. Parvin, "Harmonic cut and regularized centroid transform for localization of subcellular structures," IEEE Trans. Biomed. Eng., vol.50, no.4, pp. 469-475, Apr. 2003.
25. T. Lindeberg, "Feature detection with automatic scale selection," Int. J. Comput. Vis., vol.30, no.2, pp. 79-116, Nov. 1998.
26. Y. Boykov and G. Funka-Lea, "Graph cuts and efficient N-D image segmentation," Int. J. Comput. Vis., vol.70, no.2, pp. 109-131, Nov. 2006.
27. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/ max-flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach., vol.26, no.9, pp. 1124-1137, Sep. 2004.
28. Y. Boykov and M.-P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in Proc. Int. Conf. Comput. Vision, 2001, pp. 1025-1112.
29. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts," IEEE Trans. Pattern Anal. Mach. Intell., vol.23, no.11, pp. 1222-1239, Nov. 2001.
30. V. Kolmogorov and R. Zabih, "What energy functions can be minimized via graph cuts?" IEEE Trans. Pattern Anal. Mach. Intell., vol.26, no.2, pp. 147-159, Feb. 2004.
31. M. Sezgin and B. Sankur, "Survey over image thresholding techniques and quantitative performance evaluation," J. Electron. Imag., vol.13, no.1, pp. 146-168, Jan. 2004.
32. R. Guo and S. M. Pandit, "Automatic threshold selection based on histogram modes and a discriminant criterion," Mach. Vis. Appl., vol.10, no.5-6, pp. 331-338, Apr. 1998.
33. J. Kittler and J. Illingworth, "On threshold selection using clustering criteria," IEEE Trans. Syst., Man, Cybern., vol.SMC-15, no.5, pp. 652-655, Sep. 1985.
34. J. Kittler and J. Illingworth, "Minimum error thresholding," Pattern Recognit., vol.19, no.1, pp. 41-47, 1986.
35. N. Otsu, "Threshold selectionmethod fromGray-level histograms," IEEE Trans. Syst., Man, Cybern., vol.SMC-9, no.1, pp. 62-66, Jan. 1979.
36. J. Lie, M. Lysaker, and X. C. Tai, "A binary level set model and some applications to Mumford-Shah image segmentation," IEEE Trans. Image Process., vol.15, no.5, pp. 1171-1181, May 2006.
37. N. R. Pal and S. K. Pal, "Image model, Poisson distribution and object extraction," Int. J. Pattern Recognit. Artif. Intell., vol.5, no.3, pp. 459- 483, 1991.
38. J. L. Fan, "Notes on Poisson distribution-based minimum error thresholding," Pattern Recognit. Lett., vol.19, no.5-6, pp. 425-431, Apr. 1998.
39. N. R. Pal and D. Bhandari, "On object background classification," Int. J. Syst. Sci., vol.23, no.11, pp. 1903-1920, Nov. 1992.
40. Y. Boykov,O. Veksler, and R. Zabih, "Markov random fieldswith efficient approximations," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 1998, pp. 648-655.
41. D. Geiger,A.Gupta, L. A. Costa, and J.Vlontzos, "Dynamic programming for detecting, tracking, and matching deformable contours (vol 17, pg 294 1995)," IEEE Trans. Pattern Anal. Mach. Intell., vol.18, no.5, pp. 575- 575, May 1996.
42. J. Gill, H. Breu, D. Kirkpatrick, and M. Werman, "Linear time Euclidean distance transform algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol.17, no.5, pp. 529-533, May 1995.
43. O. Cuisenaire, "Fast Euclidean distance transformations by propagation using multiple neighbourhoods," Comput. Vis. Image Understanding, vol.76, no.2, pp. 163-172, Nov. 1999.
44. R. W. Mackin, B. Roysam, T. J. Holmes, and J. N. Turner, "Automated three-dimensional image analysis of thick and overlapped clusters in cytologic preparations. Application to cytologic smears," Anal. Quant. Cytol. Histol., vol.15, no.6, pp. 405-417, Dec. 1993.
45. R.W. Mackin, Jr., L.M. Newton, J. N. Turner, and B. Roysam, "Advances in high-speed, three-dimensional imaging and automated segmentation algorithms for thick and overlapped clusters in cytologic preparations. Application to cervical smears," Anal. Quant. Cytol. Histol., vol.20, no.2, pp. 105-121, Apr. 1998.
46. B. Roysam, H. Ancin, A. K. Bhattacharjya, M. A. Chisti, R. Seegal, and J. N. Turner, "Algorithms for automated characterization of cell populations in thick specimens from 3-D confocal fluorescence microscopy data," J. Microsc., vol.173, no.2, pp. 115-126, Feb. 1994.
47. X. W. Wu, Y. D. Chen, B. R. Brooks, and Y. A. Su, "The local maximum clustering method and its application in microarray gene expression data analysis," EURASIP J. Appl. Signal Process., vol.2004, no.1, pp. 53-63, Jan. 1, 2004.
48. L. Vincent and P. Soille, "Watersheds in digital spaces-an efficient algorithm based on immersion simulations," IEEE Trans. Pattern Anal.Mach. Intell., vol.13, no.6, pp. 583-598, Jun. 1991.
49. N. Robertson, D. P. Sanders, P. Seymour, and R. Thomas, "Efficiently four-coloring planar graphs," in Proc. 28th Annu. ACM Symp. Theory Comput., 1996, pp. 571-575.
50. D. Baggett, M. A. Nakaya,M.McAuliffe, T. P.Yamaguchi, and S. Lockett, "Whole cell segmentation in solid tissue sections," Cytometry A, vol.67, no.2, pp. 137-143, Oct. 2005.
51. C. S. Bjornsson, G. Lin, Y. Al-Kofahi, A. Narayanaswamy, K. L. Smith, W. Shain, and B. Roysam, "Associative image analysis: a method for automated quantification of 3D multi-parameter images of brain tissue," J. Neurosci. Methods, vol.170, no.1, pp. 165-178, May 15, 2008.