A Hybrid 3D Watershed Algorithm Incorporating Gradient Cues and Object Models for Automatic Segmentation of Nuclei in Confocal Image Stacks

Cytometry Part A

Volumn 56, Issue 1, 2003, Pages 23-36

  Lin, Gang ^a,b   Adiga, Umesh ^a,b   Olson, Kathy ^b   Guzowski, John F ^b   Barnes, Carol A ^b   Roysam, Badrinath ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

3D image analysis; Confocal microscopy; Model based segmentation; Nucleus segmentation; Watershed segmentation

Indexed keywords

FLUORESCENT DYE;

ALGORITHM; ARTICLE; CELL NUCLEUS; CELL NUCLEUS SEGMENTATION; CELL SHAPE; CELL SIZE; CELL STRUCTURE; CONFOCAL MICROSCOPY; FLUORESCENCE; FLUORESCENCE IN SITU HYBRIDIZATION; GEOMETRY; IMAGE ANALYSIS; MATHEMATICAL MODEL; PRIORITY JOURNAL; SCORING SYSTEM; STATISTICAL MODEL; VALIDATION PROCESS; WATERSHED ALGORITHM;

EID: 0348134935     PISSN: 15524922     EISSN: None     Source Type: Journal    
DOI: 10.1002/cyto.a.10079     Document Type: Article

References (66)

1. Belien JAM, van Ginkel AHM, Tekola P, Ploeger LS, Poulin NM, Baak JPA, van Diest PJ. Confocal DNA cytometry: a contour-based segmentation algorithm for automated three-dimensional image segmentation. Cytometry 2002:49:12-21.
2. Czader M, Liljeborg A, Auer G, Porwit A. Confocal 3-dimensional DNA image cytometry in thick tissue sections. Cytometry 1996;25:246-253.
3. Lockett SJ, Sudar D, Thompson CT, Pinkel D, Gray JW. Efficient, interactive, three-dimensional segmentation of cell nuclei in thick tissue sections. Cytometry 1998;31:275-286.
4. Rodenacker K, Aubele M, Hutzler P, Adiga U. Groping for quantitative digital 3-D image analysis: an approach to quantitative fluorescence in situ hybridization in thick tissue sections of prostate carcinoma. Anal Cell Pathol 1997;15:19-29.
5. Rigaut JP, Vassy J, Herlin P, Duigou F, Masson E, Briane D, Foucrier J, Carvajal-Gonzalez S, Downs AM, Mandard AM. Three dimensional DNA image cytometry by confocal scanning laser microscopy in thick tissue blocks. Cytometry 1991;12:511-524.
6. Ancin H, Roysam B, Dufresne TE, Chesnut MM, Ridder GM, Szarowski DH, Turner JN. Advances in automated 3-D image analysis of cell populations imaged by confocal microscopy. Cytometry 1996;25:221-234.
7. Adiga U, Chaudhuri BB. An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images. Pattern Recognition 2001;34:1449-1458.
8. Tekola P, Baak JPA, van Ginkel AHM, Belien JAM, van Diest PJ, Broeckaert MAM. Three-dimensional confocal laser scanning DNA ploidy cytometry in thick histological sections. J Pathol 1996;180:214-222.
9. Solorzano CO, Rodriguez EG, Jones A, Pinkel D, Gray JW, Sudar D, Lockett SV. Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J Microscopy 1999;193:212-226.
10. Sarti A, Solorzano CO, Lockett SJ, Malladi R. A geometric model for 3-D confocal image analysis. IEEE Trans Biomed Eng 2000;47:1600-1609.
11. Lockett SJ, O'Rand M, Rinehart C, Kaufman DG, Herman B, Jacobson K. Automated fluorescence image cytometry: DNA quantification and detection of chiamydial infections. Anal Quant Cytol 1991;13:27-44.
12. Glasbey CA. An analysis of histogram-based thresholding algorithms. CVGIP: Graphical Models and Image Processing 1993;55:532-537.
13. MacAulay C, Palcic B. A comparison of some quick and simple threshold selection methods for stained cells. Anal Quant Cytol Histol 1988;10:134-138.
14. Haralick RM, Shapiro LG. Image segmentation techniques. CVGIP 1985;29:100-133.
15. Zucker S. Region-growing: childhood and adolescence. Comput Graphics Image Process 1976;5:382-399.
16. Kohler R. A segmentation system based on thresholding. Comput Graphics Image Process 1981;15:319-338.
17. Visscher DW, Zarbo RJ, Greenawald KA, Crissman JD. Prognostic significance of morphological parameters and flow cytometric DNA analysis in carcinoma of the breast. Pathol Annu 1990;25:171-210.
18. Wolf G. Use of global information and a priori knowledge for segmentation of objects: algorithms and applications. Proceedings of the SPIE 1992;1660:397-408.
19. Ahrens P, Schleicher A, Zilles K, Werner L. Image analysis of Nissi-stained neuronal perikarya in the primary visual cortex of the rat: automatic detection and segmentation of neuronal profiles with nuclei and nucleoli. J Microscopy 1990;157:349-365.
20. Garbay C, Chassery JM, Brugal G. An interactive region-growing process for cell image segmentation based on local color similarity and global shape criteria. Anal Quant Cytol Histol 1986;8:25-34.
21. Lockett SJ, Herman B. Automatic detection of clustered, fluorescent-stained nuclei by digital image-based cytometry. Cytometry 1994;17:1-12.
22. Meyer F, Beucher S. Morphological segmentation. J Vis Commun Image Representation 1990;1:21-46.
23. Beucher S. Watershed: hierarchical segmentation and waterfall algorithm. In: Serra J, Soille P, editors. Mathematical morphology and its applications to image processing. Dordrecht, The Netherlands: Kluwer Academic Publishers; 1994. p 69-76.
24. Beucher S. The watershed transformation applied to image segmentation. Scanning Microsc 1992;6:299-314.
25. Beucher S, Meyer F. The morphological approach to segmentation: the watershed transformation. In: Mathematical morphology in image processing. New York: Marcel Dekker Inc.; 1993.
26. Vincent L. Morphological gray scale recognition in image analysis: applications and efficient algorithms. IEEE Trans Image Process 1993;2:176-201.
27. Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models: their training and application. Comput Vis Image Underst 1995;61:38-59.
28. Najman L, Schmitt M. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996;18:1163-1173.
29. Digabel H, Lantuejoul C. Iterative algorithms. Chermant JL, editor. Stuttgart, Germany: Riederer Verlag; 1987. p 85-99.
30. Beucher S. Watersheds of functions and picture segmentation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Paris; 1982. p 1928-1931.
31. Maisonneuve F. Sue le partage des eaux. Tech Report CMM. Paris: School of Mines; 1982.
32. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991;13:583-598.
33. Sijbers J, Scheunders P, Verhoye M, van der Linden A, van Dyck D, Raman E. Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imaging 1997;15:679-688.
34. Higgins WE, Ojard EJ. Interactive morphological watershed analysis for 3D medial images. Comput Med Imaging Graph 1993;17:387-395.
35. Garbay C. Image structure representation and processing: a discussion of some segmentation methods in cytology. IEEE Transactions on Pattern Analysis and Machine Intelligence 1986;8:140-146.
36. Malpica N, Solorzano CO, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, Pozo F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 1997;28:239-297.
37. Theodoridis S, Koutroumbas K. Pattern recognition. San Diego: Academic Press; 1999. 625 p.
38. Harner EJ, Slater PB. Identifying medical regions using hierarchical clustering. Soc Sci Med 1980;14D:3-10.
39. Filzmoster P, Baumgarner R, Moser E. A hierarchical clustering method for analyzing functional MR images. Magn Reson Imaging 1999;17:817-826.
40. Roysam B, Ancin H, Bhattacharjya AK, Chisti MA, Seegal R, Turner JN. Algorithms for automated characterization of cell populations in thick specimens from 3-D confocal fluorescence microscopy data. J Microscopy 1994;173:115-126.
41. Mackin RW, Roysam B, Holmes TJ, Turner JN. Automated three-dimensional image analysis of thick and overlapped clusters in cytologic preparations: application to cytologic smears. Anal Quant Cytol Histol 1993;15:405-417.
42. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;SMC-9:62-66.
43. Hader DP. Image analysis: methods and applications. Boca Raton, FL: CRC Press; 2001. 463 p.
44. Vemuri BC, Radisavljevic A. Multiresolution stochastic hybrid shape models with fractal priors. ACM Transactions on Graphics 1994;13:177-200.
45. Staib LH, Duncan JS. Model-based deformable surface finding for medical images. IEEE Trans Med Imaging 1996;15:1-12.
46. McInerney T, Terzopoulos D. Deformable models in medical image analysis: a survey. Med Image Anal 1996;1:91-108.
47. Borgefors G. Distance transformations in digital images. Comput Vis Graphics Image Proc 1986;34:344-371.
48. Haralick RM, Shapiro LG. Computer and robot vision. New York: Addison-Wesley; 1992.
49. Pawley JB. Handbook of biological confocal microscopy. New York: Plenum Press; 1995. 632 p.
50. Castleman K. Digital Image Processing. Upper Saddle River: NJ. Prentice-Hall; 1996. 667 p.
51. Zucker S, Hummel RA. A three dimensional edge operator. IEEE Trans Pattern Anal Mach Intell 1981;3:324-331.
52. Ancin H. 3-D image processing algorithms for automated cell counting, measurement and population analysis. PhD thesis. Troy, NY: Rensselaer Polytechnic Institute; 1995.
53. Parker JR. Practical computer vision using C. New York: John Wiley & Sons; 1994. 476 p.
54. Bolender RP, Charleston JS. Software for counting cells and estimating structural volumes with the optical disector and fractionator. Microsc Res Tech Vol. 25;1993. p 314-324.
55. Gundersen HJG. Stereology of arbitrary particles. J Microsc. 1986;143:3-45.
56. Marko M, Leith A, Parsons D. Three-dimensional reconstruction of cells from serial sections and whole-cell mounts using multilevel contouring of stereo micrographs. J Electron Microsc Tech 1988;9:395-411.
57. Marko M, Leith A. Contour based 3-D surface reconstruction using stereoscopic contouring and digitized images. In: Kriete A, editor. Visualization in biomedical microscopies. New York: VCH Press; 1992. p 45-74.
58. Russ JC. Practical stereology. New York: Plenum Press; 1986. 196 p.
59. West MJ. New stereological methods for counting neurons. Neurobiol Aging 1993;14:275-285.
60. West MJ. Regionally specific loss of neurons in the aging human hippocampus. Neurobiol Aging 1993;14:287-293.
61. Liljeborg A, Czader M, Porwit A. A method to compensate for light attenuation with depth in three-dimensional DNA image cytometry using confocal scanning laser microscope. J Microscopy 1995;177:108-114.
62. 4/3) computing time and O(N) memory requirements for N voxels. J Microscopy 1996;182:121-132.
63. Strasters KC, Van der Voort HTM, Geusebroek JM, Smeulders AWM. Fast attenuation correction in fluorescence confocal imaging: a recursive approach. Bioimaging 1994;2:78-92.
64. Guzowski JF, Worley PF. Cellular compartment analysis of temporal activity by fluorescence in situ hybridization (catFISH). In: Taylor GP, editor. Current protocols in neuroscience. New York: John Wiley & Sons; 2001. p 1.8.1-1.8.16.
65. Lockett SJ, Herman B. Automatic detection of clustered, fluorescent-stained nuclei by digital image-based cytometry. Cytometry 1994;17:1-12.
66. Mackin Robert W Jr, Newton Louise M, Turner James N, Roysam Badrinath. 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 1998;20:105-121.