대한의용생체공학회:학술대회논문집 (Proceedings of the KOSOMBE Conference)
- 대한의용생체공학회 1997년도 춘계학술대회
- /
- Pages.290-294
- /
- 1997
신경회로망을 이용한 염색체 영상의 최적 패턴 분류기 구현
Implementation on Optimal Pattern Classifier of Chromosome Image using Neural Network
- 장용훈 (동아대학교 공과대학 전기공학과) ;
-
이권순
(동아대학교 공과대학 전기공학과) ;
- 정형환 (동아대학교 공과대학 전기공학과) ;
- 엄상희 (부산대학교 공과대학 전자공학과) ;
- 이영우 (부산대학교 의과대학 신경외과) ;
-
전계록
(부산대학교 병원 의공학과)
- Chang, Y.H. (Dept. of Electrical Eng., Dong A University) ;
-
Lee, K.S.
(Dept. of Electrical Eng., Dong A University) ;
- Chong, H.H. (Dept. of Electrical Eng., Dong A University) ;
- Eom, S.H. (Dept. of Electronic Eng., Pusan National University) ;
- Lee, Y.W. (Dept of Neurosurgery, College of Medicine, Pusan National University) ;
-
Jun, G.R.
(Dept. of Biomedical Eng., Pusan National Univ. Hospital)
- 발행 : 1997.05.23
초록
Chromosomes, as the genetic vehicles, provide the basic material for a large proportion of genetic investigations. 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 for 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 two-step multi-layer neural network(TMANN). We are employed three morphological feature parameters ; centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.), as input in neural network by preprocessing twenty human chromosome images. The results of our experiments show that our TMANN classifier is much more useful in neural network learning and successful in chromosome classification than the other classification methods.
키워드