A curvature-based multiresolution automatic karyotyping system
Machine Vision and Applications
Joint Classification and Pairing of Human Chromosomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
New features for automatic classification of human chromosomes: A feasibility study
Pattern Recognition Letters
Iterative Contextual Recurrent Classification of Chromosomes
Neural Processing Letters
Chromosome classification using dynamic time warping
Pattern Recognition Letters
Computer Methods and Programs in Biomedicine
Fast chromosome karyotyping by auction algorithm
International Journal of Bioinformatics Research and Applications
Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme
Journal of Biomedical Informatics
Automatic segmentation and disentangling of chromosomes in Q-band prometaphase images
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Automatic locating the centromere on human chromosome pictures
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Automatic chromosome classification using medial axis approximation and band profile similarity
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Optimization of image processing techniques using neural networks: a review
WSEAS Transactions on Information Science and Applications
Image analysis pipeline for automatic karyotyping
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
A review of thresholding strategies applied to human chromosome segmentation
Computer Methods and Programs in Biomedicine
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The application of neural networks (NNs) to automatic analysis of chromosome images is investigated in this paper. All aspects of the analysis, namely segmentation, feature description, selection and extraction, and classification, are studied. As part of the segmentation process, the separation of clusters of partially occluded chromosomes, which is the critical stage that state-of-the-art chromosome analyzers usually fail to accomplish, is performed. First, a moment representation of the image pixels is clustered to create a binary image without a need for threshold selection. Based on the binary image, lines connecting cut points imply possible separations. These hypotheses are verified by a multilayer perceptron (MLP) NN that classifies the two segments created by each separating line. Use of a classification-driven segmentation process gives very promising results without a need for shape modeling or an excessive use of heuristics. In addition, an NN implementation of Sammon's mapping using principal component based initialization is applied to feature extraction, significantly reducing the dimensionality of the feature space and allowing high classification capability. Finally, by applying MLP based hierarchical classification strategies to a well-explored chromosome database, we achieve a classification performance of 83.6%. This is higher than ever published on this database and an improvement of more than 10% in the error rate. Therefore, basing a chromosome analysis on the NN-based techniques that are developed in this research leads toward a completely automatic human chromosome analysis