A Quantitative Analysis for Pre-processing Algorithm of Aberration Chromosome Observation / 대한의료정보학회지

The task of chromosome analysis is the classification of human chromosomes. The feature parameter of chromosome is very important information for chromosome analysis. The special preprocessing algorithm is required to extracting them. In this paper, we performed quantitative analysis for preprocessing algorithm of observation of chromosomal aberrations. Two algorithms is used MAT and reconstruction. The morphological feature parameters were centromeric index(C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.), and the density and width profiles. The reconstruction of chromosome images by this reconstruction algorithm was appeared as effective algorithms to observe and extract chromosome parameter.

A Quantitative Analysis for Pre-processing Algorithm of Aberration Chromosome Observation / 대한의료정보학회지

The task of chromosome analysis is the classification of human chromosomes. The feature parameter of chromosome is very important information for chromosome analysis. The special preprocessing algorithm is required to extracting them. In this paper, we performed quantitative analysis for preprocessing algorithm of observation of chromosomal aberrations. Two algorithms is used MAT and reconstruction. The morphological feature parameters were centromeric index(C.I.), relative length ratio(R.L.), relative area ratio(R.A.) and chromosome length(C.L.), and the density and width profiles. The reconstruction of chromosome images by this reconstruction algorithm was appeared as effective algorithms to observe and extract chromosome parameter.

The Implementation of Pattern Classifier for Karyotype Classification / 대한의료정보학회지

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 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.1.), 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.