Image Analysis for Automatic Segmentation of Cytoplasms and Classification of Rac1 Activation

Cytometry Part A

Volumn 57, Issue 1, 2004, Pages 22-33

  Lindblad, Joakim ^a,c   Wählby, Carolina ^a   Bengtsson, Ewert ^a   Zaltsman, Alla ^b  

^a UPPSALA UNIVERSITY   (Sweden)

^b GENERAL ELECTRIC COMPANY   (United States)

^c Centre for Image Analysis   (Sweden)

Author keywords

Automatic image analysis; Classification; Cytoplasm segmentation; Intracellular translocation; Rac; Ruffling

Indexed keywords

GREEN FLUORESCENT PROTEIN; RAC1 PROTEIN;

ANIMAL CELL; ARTICLE; AUTOANALYSIS; CELL ULTRASTRUCTURE; CELLULAR DISTRIBUTION; CHO CELL; IMAGE ANALYSIS; INTRACELLULAR TRANSPORT; NONHUMAN; PRIORITY JOURNAL;

EID: 0347988265     PISSN: 15524922     EISSN: None     Source Type: Journal    
DOI: 10.1002/cyto.a.10107     Document Type: Article

References (44)

1. Didsbury J, Weber RF, Bokoch G, Evans T, Snyderman R. Rac, a novel ras-related family of proteins that are botulinum toxin substrates. J Biol Chem 1989;264:16378-16382.
2. Kjoller L, Hall A. Signaling to rho GTPases. Exp Cell Res 1999;253:166-179.
3. Azuma T, Witke W, Stossel TP, Hartwig JH, Kwiatkowski DJ. Gelsolin is a downstream effector of rac for fibroblast motility. EMBO J 1998;17:1362-1370.
4. Miki H, Suetsugu S, Takenawa T. WAVE, a novel WASP-family protein involved in actin reorganization induced by Rac. EMBO J 1998;17:6932-6941.
5. Wählby C, Lindblad J, Vondrus M, Bengtsson E, Björkesten L. Algorithms for cytoplasm segmentation of fluorescence labelled cells. Anal Cell Pathol 2002;24:101-111.
6. Beucher S. The watershed transformation applied to image segmentation. Scanning Microsc 1992;6:299-314.
7. Vincent L, Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 1991;13:583-597.
8. Vincent L. Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms. IEEE Trans Image Process 1993;2:176-201.
9. Simon I, Pound CR, Partin AW, Clemens JQ, Christens-Barry WA. Automated image analysis system for detecting boundaries of live prostate cancer cells. Cytometry 1998;31:287-294.
10. Wu K, Gauthier D, Levine MD. Live cell image segmentation. IEEE Trans Biomed Eng 1995;42:1-12.
11. Ortiz de Solorzano C, Malladi R, Lelievre S, Lockett S. Segmentation of nuclei and cells using membrane related protein markers. J Microsc 2001;201:404-415.
12. Beliën JAM, van Ginkel HAHM, 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.
13. Malpica N, Ortiz de Solorzano C, Vaquero JJ, Santos A, Vallcorba I, Garcia-Sagredo JM, del Pozo F. Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 1997;28:289-297.
14. Umesh Adiga PS, Chaudhuri BB. An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images. Pattern Recog 2001;34:1449-1458.
15. Breu H, Gil J, Kirkpatrick D, Werman M. Linear time Euclidean distance transform algorithms. IEEE Trans Pattern Anal Mach Intell 1995;17:529-533.
16. Ranefall P, L. Egevad BN, Bengtsson E. A new method for segmentation of color images applied to immunohistochemically stained cell nuclei. Anal Cell Pathol 1997;15:145-156.
17. Rodenacker K, Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 2003;25:1-36.
18. Duda RO, Hart PE. Pattern classification and scene analysis. New York: John Wiley & Sons; 1973.
19. Raudys SJ, Jain AK. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 1991;13:252-264.
20. Dash M, Liu H. Feature selection for classification. Intell Data Anal 1997;1:131-156.
21. Queiros CE, Gelsema ES. A note on some feature selection criteria. Pattern Recog Lett 1989;10:155-158.
22. Pudil P, Ferri FJ, Novovičová J, Kittler J. Floating search methods for feature selection with nonmonotonic criterion functions. In: Proceedings of the 12th International Conference on Pattern Recognition (ICPR). 1994. p 279-283.
23. Pudil P, Novovičová J, Kittler J. Floating search methods in feature-selection. Pattern Recog Lett 1994;15:1119-1125.
24. Ferri F, Pudil P, Hatef M, Kittler J. Comparative study of techniques for large-scale feature selection. In: Gelsema ES, Kanal LN, editors. Pattern recognition in practice IV: multiple paradigms, comparative studies and hybrid systems. New York: Elsevier; 1994. p 403-413.
25. Jain AK, Zongker D. Feature selection: Evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 1997;19:153-158.
26. Kudo M, Somol P, Pudil P, Shimbo M, Sklansky J. Comparison of classifier-specific feature selection algorithms. In: Ferri F, Iñesta J, Amin A, Pudil P, editors. Advances in pattern recognition. Joint IAPR International Workshops SSPR 2000 and SPR 2000; LNCS, Alicante, Spain. Volume 1876. New York: Springer-Verlag; 2000. p 677-686.
27. Habbema JDF, Hermans J. Selection of variables in discriminant analysis by F-statistic and error rate. Technometrics 1977;19:487-493.
28. Fleiss JL, Cohen J, Everitt BS. Large sample standard errors of kappa and weighted kappa. Psychol Bull 1969;72:323-327.
29. Costanza MC, Afifi AA. Comparison of stopping rules in forward stepwise discriminant analysis. J Am Stat Assoc 1979;74:777-785.
30. Gilles S, Brady M, Declerck J, Thirion J, Ayache N. Bias field correction of breast MR images. In: Proceedings of the 4th International Conference on Visualization in Biomed Comput (VBC); Hamburg, Germany. New York: Springer-Verlag; 1996. p 153-158.
31. Lindblad J, Bengtsson E. A comparison of methods for estimation of intensity nonuniformities in 2D and 3D microscope images of fluorescence stained cells. In: Proceedings of the 12th Scandinavian Conference on Image Analysis (SCIA); Bergen, Norway. 2001. p 264-271.
32. Lancaster P, Šalkauskas K. Curve and surface fitting, an introduction. London: Academic Press; 1986.
33. Arcelli C, Sanniti di Baja G. Computing Voronoi diagrams in digital pictures. Pattern Recog Lett 1986;4:383-389.
34. Schulerud, H, Albregtsen F. Many are called, but few are chosen, feature selection and error estimation in high dimensional spaces. Comput Methods Programs Biomed. Forthcoming.
35. Mardia KV. Measures of multivariate skewness and kurtosis with applications. Biometrika 1970;57:519-530.
36. Prokop RJ, Reeves AP. A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP Graph Models Image Process 1992;54:438-460.
37. Dorst L, Smeulders AWM. Length estimators for digitized contours. Comput Vis Graphics Image Process 1987;40:311-333.
38. Kulpa Z. Area and perimeter measurement of blobs in discrete binary pictures. Comput Graphics Image Process 1977;6:434-454.
39. Barber CB, Dobkin DP, Huhdanpaa H. The quickhull algorithm for convex hulls. ACM Trans Math Software 1996;22:469-483.
40. Rencher AC, Larson SF. Bias in Wilks's A in stepwise discriminant analysis. Technometrics 1980;22:349-356.
41. Guorong X, Peiqi C, Minhui W. Bhattacharyya distance feature selection. In: Proceedings of the 13th International Conference on Pattern Recognition (ICPR). Volume 2. New York: IEEE; 1996. p 195-199.
42. Stenkvist B, Bengtsson E, Eriksson O, Jarkrans T, Nordin B, Westman-Naeser S. Histopathological systems of breast cancer classification: reproducibility and clinical significance. J Clin Pathol 1983;36:392-398.
43. Murray GD. A cautionary note on selection of variables in discriminant analysis. Appl Stat 1977;26:246-250.
44. Stone M. Cross-validation choice and assessment of statistical predictions. J R Stat Soc 1974;B36:111-147.