Algorithms for Chromosome Classification

Abstract

Automated chromosome classification has been an important pattern recognition problem for decades. In order to im-prove the performance of automated chromosome classification, artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems. This paper is focused on these algorithms, especially on artificial neural network (ANN) and wavelet transform algorithms. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed.

Share and Cite:

Yan, W. and Bai, L. (2013) Algorithms for Chromosome Classification. Engineering, 5, 400-403. doi: 10.4236/eng.2013.510B081.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] M. Zardoshti-Kermani and A. Afshordi, “Classification of Chromosomes Using Higher-Orde Neural Netorks”.
[2] O. Sjahputera and J. M. Keller, “Evolution of a Fuzzy Rule-Based System for Automatic Chromosome Recognition,” IEEE International Fuzzy System Conference Proceedings, 1999, pp. 129-134.
[3] P. H. Sydenham and R. thorn, “Handbook of Measuring System Design,” John Wiley & Sons, Ltd., 2005. http://dx.doi.org/10.1002/0471497398
[4] J. Cho, “Chromosome Classification Using Backpropagation Neural Networks,” IEEE Engineering in Medicine and Biology Magazine, Vol. 19, 2000, pp. 28-33. http://dx.doi.org/10.1109/51.816241
[5] J. Cho, S. Y. Ryu and S. H. Woo, “A Study for the Hierarchical Artificial Neural Network Model for Giemsa-Stained Human Chromosome Classification,” Proceeding of the 26th Annual International Conference of the IEEE EMBS, 2004, pp. 4588-4591.
[6] X. Ruan, “A Classifier with the Fuzzy Hopfield Network for Human Chromosomes, Intelligent Control and Automation,” Proceedings of the 3rd World Congress on Intelligent Control and Automation, Vol. 2, 2000, pp. 1159- 1164.
[7] C. S. Burrus, R. A. Gopinath and H. Guo, “Introduction to Wavelets and Wavelet Transforms,” Prentice-Hall, Englewood Cliffs, NJ, 1997.
[8] Q. Wu and K. R. Castleman, “Automated Chromosome Classification Using Wavelet-Based Band Pattern Descriptors,” 13th IEEE Symposium on Computer-Based Medical Systems, 2000, pp. 189-194.
[9] L. V. Guimaraes, J. A. Schuck and A. Elbern, “Chromosome Classification for Karyotype Composing Applying Shape Representation on Wavelet Packet Transform,” Proceedings of the 25th Annual International Conference of the IEEE EMBS, 2003, pp. 941-943.
[10] X. L. Wu, P. Biyani and S. Dumitrescu, “Globally Optimal Classification and Pairing of Human Chromosomes,” Proceedings of the 26th Annual International Conference of the IEEE EMBS, 2004, pp. 2789-2792.
[11] M. R. Speicher, S. G. Ballard and D. C. Ward, “Karyotyping Human Chromosomes by Combinatorial Multifluor FISH,” Nature Genetics, Vol. 12, 1996, pp. 368-375. http://dx.doi.org/10.1038/ng0496-368
[12] C. S. Wade, C. B. Alan and L. E. Brian, “Maximum- Likelihood Techniques for Joint Segmentation-Classification of Multispectral Chromosome Images,” IEEE Transaction on Medical Imaging, Vol. 24, No. 12, 2005, pp. 1593-1610. http://dx.doi.org/10.1109/TMI.2005.859207
[13] H. Choi, K. R. Castleman and A. C. Bovik, “Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images,” IEEE International Conference on Image Processing, 2006, pp. 69-72.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.