Chromosome classification based on the band profile similarity along approximate medial axis

Pattern Recognition

Volumn 41, Issue 1, 2008, Pages 77-89

  Kao, Jau hong ^a   Chuang, Jen hui ^a   Wang, Tsaipei ^a  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

Author keywords

Chromosome classification; Medial axis; Sequence matching; Sequence similarity

Indexed keywords

ADAPTIVE SYSTEMS; APPROXIMATION THEORY; CHROMOSOMES; DATABASE SYSTEMS;

BAND PROFILE; CHROMOSOME CLASSIFICATION; SEQUENCE SIMILARITY; SUBSEQUENCE MATCHING;

FEATURE EXTRACTION;

EID: 34548026554     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2007.05.016     Document Type: Article

References (34)

1. Lerner B. Toward a completely automatic neural-network-based human chromosome analysis. IEEE Trans. Systems Man Cybern. 28 (1998) 544-552
2. 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
3. Garcia C.U., Rubio A.B., Perez F.A., and Hernandez F.S. A curvature-based multiresolution automatic karyotyping system. Mach. Vision Appl. 14 (2003) 145-156
4. Guimaraes L.V., Schuck A., and Elbern A. Chromosome classification for karyotype composing applying shape representation on wavelet packet transform. Proceedings of the 25th Annual International Conference on IEEE EMBS (2003), Cancun, Mexico 941-943
5. ISCN1985: An international system for human cytogenetics nomenclature, in: D.G. Harnden, H.P. Klinger (Eds.), Cytogenetics and Cell Genetics, Karger, Basel, 1985.
6. Piper J., and Granum E. On fully automatic feature measurements for banded chromosome classification. Cytometry 10 (1989) 242-255
7. Charters G.C., and Granum E. Trainable grey-level models for disentangling overlapping chromosomes. Pattern Recognition 32 (1999) 1335-1349
8. Carothers A., and Piper J. Computer-aided classification of human chromosomes: a review. Stat. Comput. 4 (1994) 161-171
9. Lerner B., Guterman H., Dinstein I., and Romem Y. Medial axis transform based features and a neural network for human chromosome classification. Pattern Recognition 28 (1995) 1673-1683
10. Ritter G., and Schreib G. Using dominant points and variants for profile extraction from chromosomes. Pattern Recognition 34 (2001) 923-938
11. Groen C.A., Kate K., Smeulders A.W.M., and Young T. Human chromosome classification based on local band descriptors. Pattern Recognition Lett. 9 (1989) 211-222
12. G. Ritter, G. Schreib, Profile and feature extraction from chromosomes, in: Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, Barcelona, Spain, 2000, pp. 287-290.
13. Moller A., Nilsson H., Caspersson T., and Lomakka G. Identification of human chromosome regions by aid of computerized pattern analysis. Exp. Cell Res. 70 (1972) 475-478
14. Granum E., Gerdes T., and Lundsteen C. Simple weighted density distributions, WDD's for discrimination between G-banded chromosomes. Proceedings of the Fourth European Chromosome Analysis Workshop (1981), Edinburgh, Scotland
15. Lundsteen C., Gerdes T., and Maahr J. Automatic classification of chromosomes as part of a routine system for clinical analysis. Cytometry 7 (1985) 1-7
16. Gregor J., and Granum E. Finding chromosome centromeres using band pattern information. Comput. Biol. Med. 21 (1991) 55-67
17. Sweeney W.P., Musavi M.T., and Guigi J.N. Classification of chromosomes using a probabilistic neural network. Cytometry 16 (1994) 17-24
18. Errington P.A., and Graham J. Application of artificial neural networks to chromosome classification. Cytometry 14 (1993) 627-639
19. Conroy J.M., Kolda T.G., O'Leary D.P., and O'Leary T.J. Chromosome identification using hidden markov models: comparison with neural networks, singular value decomposition, principal components analysis, and Fisher discriminant analysis. Lab. Invest. 80 (2000) 1629-1641
20. Biyani P., Wu X., and Sinha A. Joint classification and pairing of human chromosomes. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2 (2005) 102-109
21. Schwartzkopf W.C., Bovik A.C., and Evans B.L. Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images. IEEE Trans. Med. Imaging 24 (2005) 1593-1610
22. Lam L., Lee S.-W., and Suen C.Y. Thinning methodologies-a comprehensive survey. IEEE Trans. Pattern Anal. Mach. Intell. 14 (1992) 869-885
23. Agrawal R., Lin K.I., Sawhney H.S., and Shim K. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. Proceedings of the 21st International Conference on Very Large Databases (1995), Morgan Kaufmann, Zurich, Switzerland 490-501
24. Agrawal R., Faloutsos C., and Swami A.N. Efficient similarity search in sequence databases. In: Lomet D. (Ed). Proceedings of the Fourth International Conference on Foundations of Data Organization and Algorithms (1993), Springer, Chicago, IL 69-84
25. C. Faloutsos, M. Ranganathan, Y. Manolopoulos, Fast subsequence matching in time-series databases, in: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, Minneapolis, MN, 1994, pp. 419-429.
26. Granum E., Thomason M.G., and Gregor J. On the use of automatically inferred Markov networks for chromosome analysis. Automation of Cytogenetics (1989), Springer, Berlin 233-251
27. Granum E., and Thomason M.G. Automatically inferred Markov network models for classification of chromosomal band pattern structures. Cytometry 11 (1990) 26-39
28. Rafiei D., and Mendelzon A. Similarity-based queries for time series data. Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data (1997), Tucson, Arizona 13-25
29. Moon Y.S., Whang K.Y., and Han W.S. General match: a subsequence matching method in time-series databases based on generalized windows. Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data (2002), Madison, Wisconsin 382-393
30. Roytberg M. A search for common patterns in many sequences. Comput. Appl. Biosci. 8 (1992) 57-64
31. J.T.L. Wang, G.W. Chirn, T.G. Marr, B. Shapiro, D. Shasha, K. Zhang, Combinatorial pattern discovery for scientific data: some preliminary results, in: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, Minneapolis, MN, 1994, pp. 115-125.
32. Erickson B.W., and Sellers P.H. Recognition of patterns in genetic sequences. In: Sankoff D., and Kruskal J.B. (Eds). Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison (1983), Addison-Wesley, Reading, MA 55-90
33. D. Berndt, J. Clifford, Using dynamic time warping to find patterns in time series, in: KDD94: AAAI Workshop on Knowledge Discovery in Databases, Seatle, WA, 1994, pp. 359-370.
34. +-tree: a dynamic index for multi-dimensional objects. Proceedings of the 12th International Conference on Very Large Databases (1987), Brighton, England 507-518