20 March 2015 Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model
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Abstract
Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.
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Sijia Liu, Ruhan Sa, Orla Maguire, Hans Minderman, Vipin Chaudhary, "Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94132H (20 March 2015); doi: 10.1117/12.2081026; https://doi.org/10.1117/12.2081026
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