Automatic locating the centromere on human chromosome pictures
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Segmentation and centromere locating methods applied to fish chromosomes images
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Toward a completely automatic neural-network-based human chromosomeanalysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Subspace-based prototyping and classification of chromosome images
IEEE Transactions on Image Processing
Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme
Journal of Biomedical Informatics
A modular framework for the automatic classification of chromosomes in Q-band images
Computer Methods and Programs in Biomedicine
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Automatic centromere identification and polarity assignment are two key factors in the automatic karyotyping of human chromosomes. A multi-stage rule-based computer scheme has been investigated to automatically detect centomeres and determine polarities for both abnormal and normal metaphase chromosomes. The scheme first implements a modified thinning algorithm to identify the medial axis of a chromosome and extracts three feature profiles. Based on a set of pre-optimized classification rules, the scheme adaptively identifies the centromere and then assigns corresponding polarity. An image dataset of 2287 chromosomes acquired from 24 abnormal and 26 normal Giemsa metaphase cells is utilized to optimize and test the scheme. The overall accuracy is 91.4% for centromere identification and 97.4% for polarity assignment. The experimental results demonstrate that our scheme can be successfully applied to diverse chromosomes, which include those severely bent and abnormal chromosomes extracted from cancer cells.