Homologue Matching Applications: Recognition of Overlapped Chromosomes
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. Overlapping chromosomes cause difficulties in the automated chromosome karyotyping process. First, overlapping chromosomes must be recognised and decomposed into the proper chromosome parts. Secondly, the decomposed chromosomes must be classified. The first difficulty is associated with image segmentation. The second area is a pattern recognition problem. Even if chromosomes within overlapping clusters are decomposed properly, classification capability is impaired due to feature distortion in the overlapped regions. In normal human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes, 1-22, and X chromosome for females. This research presents a homologue matching approach for overlapped chromosome recognition. The undistorted grey level information in isolated chromosomes is used for identifying overlapped chromosomes. An isolated chromosome prototype is obtained using neural networks. Dynamic programming and neural networks are compared for matching the prototype to its overlapped homologue. The homologue matching method is applied to identifying chromosome 2 in 50 metaphase spreads. Experimental results showed that homologue matching using dynamic programming matching based on the density profile achieved a higher correct recognition rate than homologue matching using three different neural network approaches.
R. J. Stanley, "Homologue Matching Applications: Recognition of Overlapped Chromosomes," Pattern Analysis and Applications, vol. 1, no. 4, pp. 206-217, Springer Verlag, Dec 1998.
The definitive version is available at http://dx.doi.org/10.1007/BF01234768
Electrical and Computer Engineering
Keywords and Phrases
Dynamic programming; Human chromosomes; Image processing; Karyotyping; Medical imaging; Neural networks
International Standard Serial Number (ISSN)
Article - Journal
© 1998 Springer Verlag, All rights reserved.