• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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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.

• 한국약용작물학회지
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• 제11권5호
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• pp.402-407
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• 2003

개시호 (Bulpleurum longeradiatum)를 대상으로 상염 색법과 FISH기법을 통한 염색체 분석을 통하여 다음과 같은 결과를 얻었다. 개시호의 체세포 염색체 수는 2n=12이었으며, centromeric index를 전중기 염색체를 이용한 핵형 분석에서 염색체 조성은 3쌍의 중부 염색체 (3번, 4번 및 6번)와 3쌍의 차중부 염색체 (1번, 2번 및 5번)로 구분되었다. 염색체의 길이는 $2.55{\sim}5.05\;{\mu}m$로, 전체 길이는 $18.15\;{\mu}m$로 나타났다. 5S 와 45S rDNA를 탐침으로 FISH를 수행한 결과 4번 염색체의 동원체 부위 에서 한 쌍의 5S rDNA signal이 확인되었고, 2번 염색체의 부수체에서 한 쌍의 45S rDNA signal이 관찰되었다.

• 대한의용생체공학회:의공학회지
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• 제18권3호
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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.