• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.133-136
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• 1997

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.

• Korean Journal of Medicinal Crop Science
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• v.11 no.5
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• pp.402-407
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• 2003

Karyotype analysis and chromosomal localization of 5S and 45S rDNAs using multi-color fluorescence in situ hybridization (McFISH) technique were carried out in Bupleurum longeradiatum. Somatic metaphase chromosome number was 2n=12. Karyotype was composed of three pairs of metacentrics (No.3, 4 and 6) and three pairs of submetacentrics (No. 1, 2 and 5). The length of somatic prometaphase chromosomes ranges from 2.55 to $5.05{\mu}m$ with total length of $18.15\;{\mu}m$. In FISH experiment, one pair of 5S rDNA signals was detected on the pericentromeric region of chromosome 4 and one pair of 45S rDNA signals was detected on the telomeric region of chromosome 2.

• Journal of Biomedical Engineering Research
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• v.18 no.3
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• pp.233-241
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• 1997

The research on chromosomes is very significant in cytogenetics since genes of the chromosomes control revelation of the inheritance plasma. The human chromosome analysis is widely used to study leukemia, malignancy, radiation hazard, and mutagen dosimetry as well as various congenital anomalies such as Down's, Klinefelter's, Edward's, and Patau's syndrome. The framing and analysis of the chromosome karyogram, which requires specific cytogenetic knowledge is most important in this field. Many researches on automated chromosome karyotype analysis methods have been carried out, some of which produced commercial systems. However, there still remains much room to improve the accuracy of chromosome classification and to reduce the processing time in real clinic environments. In this paper, we proposed a hierarchical artificial neural network(HANN) to classify the chromosome karyotype. We extracted three or four chromosome morphological feature parameters such as centromeric index, relative length ratio, relative area ratio, and chromosome length by preprocessing from ten human chromosome images. The feature parameters of five human chromosome images were used to learn HANN and the rest of them were used to classify the chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other researchers using less feature parameters.