Feature extraction of Giemsa-stained chromosomes and classification error of a back-propagation neural network-based classifier

Seung-Yun Ryu; Jongman Cho

doi:10.1117/12.461676

5 April 2002 Feature extraction of Giemsa-stained chromosomes and classification error of a backpropagation neural-network-based classifier

Seung-Yun Ryu, Jongman Cho

Author Affiliations +

Proceedings Volume 4668, Applications of Artificial Neural Networks in Image Processing VII; (2002) https://doi.org/10.1117/12.461676
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

Many studies for computer-based chromosome analysis using artificial neural network (ANN) have shown that it is possible to classify chromosomes into 24 subgroups. It is important to select optimum features for training the ANN. Our purpose was to select features that had the low classification error and the best ability for human chromosome classification. We applied the medial axis transformation for the medial line, extended the line to the boundary and obtained relative length, relative area and centromeric index. The Giemsa-stained human chromosome has a sequence of banding pattern that is perpendicular to the medial axis of the chromosome. Density profile is a one-dimensional graph of the banding pattern property of the chromosome computed at a sequence of points along the possibly curved chromosome medial axis. Some studied used relative length, centromeric index and 62 density profile as features, but we prepared two data sets as features that one set was relative length, centromeric index and 80 density profile considered No. 1 chromosome's length and the other was relative length, centromeric index, the 80 density profile and relative area and compared classification error of each set. We found that the classification error showed to be decreased by adding relative area to the other features.

Citation Download Citation

Seung-Yun Ryu and Jongman Cho "Feature extraction of Giemsa-stained chromosomes and classification error of a backpropagation neural-network-based classifier", Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); https://doi.org/10.1117/12.461676

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