Proceedings of the KOSOMBE Conference (대한의용생체공학회:학술대회논문집)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지

The Implementation of Pattern Classifier or Karyotype Classification

핵형 분류를 위한 패턴 분류기 구현

  • Eom, S.H. (Dept. of Electronic Engineering, Pusan National University) ;
  • Nam, K.G. (Dept. of Electronic Engineering, Pusan National University) ;
  • Chang, Y.H. (Dept. of Computer Information and Processing, Dong-Ju Women's Junior College) ;
  • Lee, K.S. (Dept. of Electrical Engineering, Dong-A University) ;
  • Chang, H.H. (Dept. of Electrical Engineering, Dong-A University) ;
  • Kim, G.S. (Dept. of Computer Engineering, Pusan National University) ;
  • Jun, G.R. (Dept. of Medical Engineering, Pusan National University Hospital)
  • 엄상희 (부산대학교 전자공학과) ;
  • 남기곤 (부산대학교 전자공학과) ;
  • 장용훈 (동주여자전문대학 전산정보처리과) ;
  • 이권순 (동아대학교 전기공학과) ;
  • 정형환 (동아대학교 전기공학과) ;
  • 김금석 (부산대학교 컴퓨터공학과) ;
  • 전계록 (부산대학교 병원 의공학과)
  • Published : 1997.11.28
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Abstract

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.

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