Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei

Cytometry Part A

Volumn 63, Issue 1, 2005, Pages 20-33

  Lin, Gang ^a   Chawla, Monica K ^b   Olson, Kathy ^b   Guzowski, John F ^c   Barnes, Carol A ^b   Roysam, Badrinath ^a  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c UNIVERSITY OF NEW MEXICO HEALTH SCIENCES CENTER   (United States)

Author keywords

Cell counting; Confocal microscopy; Fluorescence in situ hybridization quantification; Hierarchical; Image segmentation; Model based; Object features; Region merging; Three dimensional image analysis; Watershed segmentation

Indexed keywords

ANIMAL TISSUE; ARTICLE; CELL NUCLEUS; COMPUTER MODEL; CONFOCAL MICROSCOPY; FLUORESCENCE IN SITU HYBRIDIZATION; HIPPOCAMPUS; IMAGE ANALYSIS; IMMEDIATE EARLY GENE; NONHUMAN; PARIETAL LOBE; PRIORITY JOURNAL; RAT; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ANIMALS; BRAIN; CELL NUCLEUS; CLUSTER ANALYSIS; COMPUTER SIMULATION; IMAGING, THREE-DIMENSIONAL; MICROSCOPY, CONFOCAL; PATTERN RECOGNITION, AUTOMATED; RATS;

EID: 13244256957     PISSN: 15524922     EISSN: None     Source Type: Journal    
DOI: 10.1002/cyto.a.20099     Document Type: Article

References (57)

1. Russ JC. Computer-assisted microscopy: the measurement and analysis of images. New York: Plenum Press; 1990.
2. Chawla MK, Lin G, Olson K, Vazdarjanova A, Burke SN, McNaughton BL, Worley PF, Guzowski JF, Roysam B, Barnes CA. 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 2004;139:13-24.
3. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 1991;13:583-598.
4. Lin G, Adiga U, Olson K, Guzowski J, Barnes C, Roysam B. A hybrid 3-D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry 2003;56A:23-36.
5. Zucker SW. Region growing: childhood and adolescence. Comput Vis Graphics Image Process 1976;5:382-399.
6. Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994;16:641-647.
7. Chang Y-L, Li X. Adaptive image region-growing. IEEE Trans Image Process 1994;3:868-872.
8. Hojjatoleslami SA, Kittler J. Region growing: a new approach. IEEE Trans Image Process 1998;7:1079-1084.
9. Horowitz S, Pavlidis T. Picture segmentation by a directed split and merge procedure. ICPR74 1974;424-433.
10. Haralick R, Shapiro L. Image segmentation techniques. CVGIP 1985; 29:100-132.
11. Liu L, S S. Region segmentation via deformable model-guided split and merge. Computer science technical report 2000 (2000-24). Boston: Bonston University; 2000.
12. Wu X. Adaptive split-and-merge segmentation based on piecewise least-square approximation. IEEE Trans Pattern Anal Mach Intell 1993; 15:808-815.
13. Trucco E, Verri A. Introductory techniques for 3-D computer vision. Upper Saddle River, NJ: Prentice-Hall; 1998.
14. Zhu SC, Yuille A. Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans Pattern Anal Mach Intell 1996;18:884-900.
15. Fan J, Yau D, Elmagarmid A, Aref W. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 2001;10:1454-1466.
16. Shafarenko L, Petrou M, Kittler J. Automatic watershed segmentation of randomly textured color images. IEEE Trans Image Process 1997; 6:1530-1544.
17. Vincent L. Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 1993; 2:176-201.
18. Beucher S. Watersheds of functions and picture segmentation. IEEE International Conference on Acoustics, Speech and Signal Processing. New York: IEEE; 1982. p 1928-1931.
19. Beucher S. The watershed transformation applied to image segmentation. Scanning Microsc 1992;6:299-314.
20. Beucher S, Meyer F. The morphological approach to segmentation: the watershed transformation, Mathematical morphology and image processing. New York: Marcel Dekker; 1993.
21. Najman L, Schmitt M. Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans Pattern Anal Mach Intell 1996; 18:1163-1173.
22. Sunil Kumar K, Desai UB. Joint segmentation and image interpretation. Pattern Recogn 1999;32:577-589.
23. Neumann A. Graphical gaussian shape models and their application to image segmentation. IEEE Trans Pattern Anal Mach Intell 2003;25:316-329.
24. Trucco E, Fisher RB. Experiments in curvature-based segmentation of range data. IEEE Trans Pattern Anal Mach Intell 1995;17:177-182.
25. Besl PJ, Jain RC. Segmentation through variable-order surface fitting. IEEE Trans Pattern Anal Mach Intell 1988;10:167-192.
26. Black MJ, Rangarajan A. On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. Int J Comput Vis 1996;19:57-91.
27. Blake A, Zisserman A. Visual reconstruction. Cambridge: MIT Press; 1987.
28. Guigues L, Le Men H, Cocquerez J-P. The hierarchy of the cocoons of a graph and its application to image segmentation. Pattern Recogn Lett 2003;24:1059-1066.
29. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 2000;22:888-905.
30. Xu Y, Uberbacher EC. 2D image segmentation using minimum spanning trees. Image Vis Comput 1997;15:47-57.
31. Moscheni F, Bhattacharjee S, Kunt M. Spatio-temporal segmentation based on region merging. IEEE Trans Pattern Anal Mach Intell 1998; 20:897-915.
32. Castleman KR. Digital image processing. Englewood Cliffs, NJ: Prentice-Hall; 1996.
33. Pleißner K-P, Hoffmann F, Kriegel K, Wenk C, Wegner S, Sahlström A, Oswald H, Alt H, Fleck E. New algorithmic approaches to protein spot detection and pattern matching in two-dimensional electrophoresis gel databases. Electrophoresis 1999;20:755-765.
34. Bleau A, Leon LJ. Watershed-based segmentation and region merging. Comput Vis Image Understand 2000;77:317-370.
35. Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK. Hybrid image segmentation using watersheds and fast region merging. IEEE Trans Image Process 1998;7:1684-1699.
36. Bernard R, Kanduser M, Pernus F. Model-based automated detection of mammalian cell colonies. Phys Med Biol 2001;46:3061-3072.
37. Ezquerra N, Mullick R. Knowledge-guided segmentation of 3D imagery. Graphic Models Image Process 1996;58:510-523.
38. Chassery J, Garbay C. An iterative segmentation method based on a contextual color and shape criterion. IEEE Trans Pattern Anal Mach Intell 1984;6:794-800.
39. Mitchell SC, Bosch JG, Lelieveldt BPF, van der Geest RJ, Reiber JHC, Sonka M. 3-D active appearance models: segmentation of cardiac MR and ultrasound images. IEEE Trans Med Imaging 2002;21:1167-1178.
40. Serra JP, Soille P. Mathematical morphology and its applications to image processing. Boston: Kluwer Academic Publishers; 1994.
41. Digabel H, Lantuejoul C. Iterative algorithms. Pract Methods 1978;8:85-99.
42. Maisonneuve F. Sue le partage des eaux. School of Mines; 1982.
43. Higgins W, Ojard E. Interactive morphological watershed analysis for 3D medial images. Comput Med Imaging Graph 1993;17:387-395.
44. 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.
45. Ancin H, Roysam B, Dufresne TE, Chestnut MM, Ridder GM, Szarowski DH, Turner JN. Advances in automated 3-D image analyses of cell populations imaged by confocal microscopy. Cytometry 1996; 25:221-34.
46. Malpica N, de Solorzano CO, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, del Pozo F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 1997;28:289-97.
47. Umesh Adiga PS, Chaudhuri BB. An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images. Pattern Recogn 2001;34:1449-1458.
48. Solorzano C, Rodriguez E. Segmentation of confocal microscope images of cell nuclei in thick tissue sections. J Microsc 1999;193:212-226.
49. Pawley JB. Handbook of biological confocal microscopy. New York: Plenum Press; 1995.
50. Karel C Strasters, Hans T M van der Voort, J M Geusebroek, Smeulders AWM. Fast attenuation correction in fluorescence confocal imaging: a recursive approach. Bioimaging 2001;2:78-92.
51. Can A, Al-Kofahi O, Lasek S, Szarowski D, Turner J, Roysam B. Attenuation correction in confocal laser microscopes: a novel two-view approach. J Microsc 2003;211:67-79.
52. Roysam B, Bhattacharjya A, Srinivas C, Turner J. Unsupervised noise removal algorithms for 3-D confocal fluorescence microscopy. Micron Microsc Acta 1992;23:447-461.
53. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Systems Man Cybernetics 1979;9:62-66.
54. Parker JR. Practical computer vision using C. New York: Wiley; 1994.
55. Theodoridis S, Koutroumbas K. Pattern recognition. San Diego: Academic Press; 1999.
56. Ballard DH, Brown CM. Computer vision. Englewood Cliffs, NJ: Prentice-Hall; 1982.
57. Guzowski JF, McNaughton BL, Barnes CA, Worley PF. Environment-specific expression of the immediate-early gene arc in hippocampal neuronal ensembles. Nat Neurosci 1999;2:1120-1124.