Iterative Contextual Recurrent Classification of Chromosomes
Neural Processing Letters
Computer Methods and Programs in Biomedicine
Automated identification of analyzable metaphase chromosomes depicted on microscopic digital images
Journal of Biomedical Informatics
Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme
Journal of Biomedical Informatics
Chromosome image recognition with subregion search iteration
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A chromosome image recognition method based on subregions
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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Chromosomes are essential genomic information carriers. Chromosome classification constitutes an important part of routine clinical and cancer cytogenetics analysis. Cytogeneticists perform visual interpretation of banded chromosome images according to the diagrammatic models of various chromosome types known as the ideograms, which mimic artists' depiction of the chromosomes. In this paper, we present a subspace-based approach for automated prototyping and classification of chromosome images. We show that 1) prototype chromosome images can be quantitatively synthesized from a subspace to objectively represent the chromosome images of a given type or population, and 2) the transformation coefficients (or projected coordinate values of sample chromosomes) in the subspace can be utilized as the extracted feature measurements for classification purposes. We examine in particular the formation of three well-known subspaces, namely the ones derived from principal component analysis (PCA), Fisher's linear discriminant analysis, and the discrete cosine transform (DCT). These subspaces are implemented and evaluated for prototyping two-dimensional (2-D) images and for classification of both 2-D images and one-dimensional profiles of chromosomes. Experimental results show that previously unseen prototype chromosome images of high visual quality can be synthesized using the proposed subspace-based method, and that PCA and the DCT significantly outperform the well-known benchmark technique of weighted density distribution functions in classifying 2-D chromosome images.