Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme

Journal of Biomedical Informatics

Volumn 42, Issue 1, 2009, Pages 22-31

  Wang, Xingwei ^a   Zheng, Bin ^b   Li, Shibo ^c   Mulvihill, John J ^c   Wood, Marc C ^a   Liu, Hong ^a  

^a University of Oklahoma   (United States)

^b UNIVERSITY OF PITTSBURGH MEDICAL CENTER   (United States)

^c UNIVERSITY OF OKLAHOMA COLLEGE OF MEDICINE   (United States)

Author keywords

Artificial neural network; Genetic algorithm; Karyotype; Metaphase chromosome; Training testing validation

Indexed keywords

ARTIFICIAL NEURAL NETWORK; GENETIC ALGORITHM; KARYOTYPE; METAPHASE CHROMOSOME; TRAINING-TESTING-VALIDATION;

AUTOMATION; BACKPROPAGATION; CHROMOSOMES; GENETIC ALGORITHMS;

NEURAL NETWORKS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; CHROMOSOME ANALYSIS; CLASSIFICATION; CONTROLLED STUDY; GENETIC ALGORITHM; HUMAN; HUMAN CELL; KARYOTYPE; METAPHASE CHROMOSOME; PRIORITY JOURNAL; VALIDATION STUDY;

ALGORITHMS; CHROMOSOMES, HUMAN; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KARYOTYPING; METAPHASE; MODELS, GENETIC; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS;

EID: 60049094075     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2008.05.004     Document Type: Article

References (42)

1. Tjio J.H., and Levan A. The chromosome number in man. Hereditas 42 (1956) 1-6
2. Conference D. A proposed standard system of nomenclature of human mitotic chromosomes. Lancet 1 (1960) 1063-1065
3. Piper J., Granum E., Rutovitz D., and Ruttledge H. Automation of chromosome analysis. Signal Process 2 (1980) 203-221
4. Wang X., Zheng B., Wood M., Li S., Chen W., and Liu H. Development and evaluation of automated systems for detection and classification of banded chromosomes: current status and future perspectives. J Phys D: Appl Phys 38 (2005) 2536-2542
5. Groen F., Kate T., Smeulders A., and Young I. Human chromosome classification based on local band descriptors. Patt Recogn Lett 9 (1989) 211-222
6. Piper J., and Granum E. On fully automatic feature measurement for banded chromosome classification. Cytometry 10 3 (1989) 242-255
7. van Vliet L.J., Young I.T., and Mayall B.H. The Athena semi-automated karyotyping system. Cytometry (1990) 51-58
8. Schwartzkopf W.C. Maximum likelihood techniques for joint segmentation-classification of multi-spectral chromosome images (2002), The University of Texas at Austin, Austin
9. Jennings A.M., and Graham J. A neural network approach to automatic chromosome classification. Phys Med Biol 38 (1993) 959-970
10. Wu Q, Castleman KR. Automated chromosome classification using wavelet-based band pattern descriptors. IEEE Symposium on Computer-Based Medical Systems CBMS 2000. Houston, TX, USA: 2000. p. 189-94.
11. Johnston D.A., Tang K.S., and Zimmerman S. Band features as classification measures for G-banded chromosome analysis. Comput Biol Med 23 2 (1993) 115-129
12. Tso M.K.S., and Graham J. The transportation algorithm as an aid to chromosome classification. Patt Recog Lett 1 (1983) 489-496
13. Lerner B., Levinstein M., Rosenberg B., and Guterman H. Feature selection and chromosome classification using a multilayer perceptron neural network, Neural Networks. IEEE Int Conf Comput Intell (1994) 3540-3545
14. Wu Q., Liu Z., Chen T., and Xiong Z.K.R.C. Subspace-based prototyping and classification of chromosome images. IEEE Trans Image Process 14 (2005) 1277-1287
15. Carothers A., and Piper J. Computer-aided classification of human chromosomes: a review. Stat Comput 4 (1994) 161-171
16. Sweeney W.P., Musavi M.T., and Guidi J.N. Classification of chromosomes using a probabilistic neural network. Cytometry 16 (1996) 17-24
17. Errington P., and Graham J. Application of artificial neural networks to chromosome classification. Cytometry 14 (1993) 627-639
18. Lerner B. Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans Syst Man Cybern-B: Cybern 28 (1998) 544-552
19. Cho J. Chromosome classification using back propagation neural networks. IEEE Eng Med Biol Mag 19 (2000) 28-33
20. Delshadpour S. Reduced size multi layer perceptron neural network for human chromosome classification. In: Proceedings of the 25th Annual International Conference of the IEEE (Engineering in Medicine and Biology Society). 2003. p. 2249-52.
21. Wu Q, Suetens P, Oosterlinck A. Chromosome classification using a multi-layer perceptron neural net. In: Proceedings of the 12th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 1990. p. 1453-54.
22. Wu Q, Suetens P, Oosterlinck A. On knowledge-based improvement of biomedical pattern recognition-a case study. In: Proceedings of 5th conference on artificial intelligence for applications. 1989. p. 239-44.
23. Lu Y, Ya Y. An expert system for banded chromosomes recognition. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society. 1989. p. 1789-90.
24. Ramstein G, Bernadet M, Kangoud A, Barba D. A rule-based image analysis system for chromosome classification. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society; 1992; 1992. p. 926-27.
25. Zimmerman S.O., Johnston D.A., Arrighi F.E., and Rupp M.E. Automated homologue matching of human G-banded chromosomes. Comput Biol Med 16 (1986) 223-233
26. Keller JM, Gader P, Sjahputera O, Caldwell CW. A fuzzy logic rule-based system for chromosome recognition. In: Proceedings of the eighth IEEE symposium on computer-based medical systems. 1995. p. 125-32.
27. Popescu M., Gader P., Keller J., and Klein C. Automatic karyotyping of metaphase cells with overlapping chromosomes. Comput Biol Med 29 (1999) 61-82
28. Stanley R.J., Keller J.M., Gader P., and Caldwell C.W. Data-driven homologue matching for chromosome identification. IEEE Trans Med Imaging 17 (1998) 451-462
29. Gregor J., and Granum E. Finding chromosome centromeres using band pattern information. Comput Biol Med 21 1-2 (1991) 55-67
30. Graham J., Errington P., and Jennings A.M. A neural network chromosome classifier. J Radiat Res 33 (1992) 250-257
31. Mitchell T.M. Machine learning (1997), WCB McGraw-Hill, Boston MA
32. Li Q. Reliable evaluation of performance level for computer-aided diagnostic scheme. Acad Radiol 14 (2007) 985-991
33. Wang X., Li S., Liu H., Wood M., Chen W., and Zheng B. Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images. J Biomed Inform 41 (2007) 264-271
34. Wang X., Zheng B., Li S., Mulvihill J.J., and Liu H. A rule-based scheme for centromere identification and polarity assignment of metaphase chromosomes. Comput Methods Programs Biomed 89 1 (2008) 33-42
35. Kramer H.P., and Bruckner J.B. Iterations of a non-linear transformation for enhancement of digital images. Patt Recogn 7 (1975) 53-58
36. Granum E. Pattern recognition aspects of chromosome analysis-computerized and visual interpretation of banded human chromosomes (1980), Tech Univ Denmark, Lyngby
37. Tseng CC. Human chromosome analysis in tested studies for laboratory teaching. In: Goldman CA, editor. In: Proceedings of the 16th Workshop/Conference of the Association for Biology Laboratory Education (ABLE). Atlanta, Georgia; 1995. p. 35-56.
38. Holland J.H. Adaption in neural and artificial systems. University of Michigan press. Ann Arbor Mich (1975)
39. Zheng B., Chang Y.H., Good W.F., and Gur D. Performance gain in computer-assisted detection schemes by averaging scores generated from artificial neural networks with adaptive filtering. Med Phys 28 (2001) 2302-2308
40. Kantrowitz M. Prime time freeware for AI, issue 1-1. Artif Intell Repository 1 1 (1994) Selected materials from the Carnegie Mellon University, Sunnyvale, CA, Prime Time Freeware
41. Li Q., and Doi K. Reduction of bias and variance for evaluation of compute-aided diagnostic schemes. Med Phys 33 (2006) 868-875
42. Shih L.M., and Wang T.L. Apply innovative technologies to explore cancer genome. Curr Opin Oncol 17 (2005) 33-38