Classification of flourescence in situ hybridization images using belief networks
Pattern Recognition Letters
Support vector machine-based image classification for genetic syndrome diagnosis
Pattern Recognition Letters
On the Classification of a Small Imbalanced Cytogenetic Image Database
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Bayesian network classification using spline-approximated kernel density estimation
Pattern Recognition Letters
Bayesian fluorescence in situ hybridisation signal classification
Artificial Intelligence in Medicine
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Fast and accurate analysis of fluorescence in-situ hybridization images for signal counting will depend mainly upon two components: a classifier to discriminate between artifacts and valid signals of several fluorophores (colors), and well discriminating features to represent the signals. Our previous work (2001) has focused on the first component. To investigate the second component, we evaluate candidate feature sets by illustrating the probability density functions and scatter plots for the features. The analysis provides first insight into dependencies between features, indicates the relative importance of members of a feature set, and helps in identifying sources of potential classification errors. Class separability yielded by different feature subsets is evaluated using the accuracy of several neural network-based classification strategies, some of them hierarchical, as well as using a feature selection technique making use of a scatter criterion. Although applied to cytogenetics, the paper presents a comprehensive, unifying methodology of qualitative and quantitative evaluation of pattern feature representation essential for accurate image classification. This methodology is applicable to many other real-world pattern recognition problems