Segmentation and classification of dot and non-dot-like fluorescence in situ hybridization signals for automated detection of cytogenetic abnormalities. |
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Authors: | Boaz Lerner Lev Koushnir Josepha Yeshaya |
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Affiliation: | Department of Electrical and Computer Engineering, Ben-Gurion University, Beer Sheva 84105, Israel. boaz@ee.bgu.ac.il |
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Abstract: | Signal segmentation and classification of fluorescence in situ hybridization (FISH) images are essential for the detection of cytogenetic abnormalities. Since current methods are limited to dot-like signal analysis, we propose a methodology for segmentation and classification of dot and non-dot-like signals. First, nuclei are segmented from their background and from each other in order to associate signals with specific isolated nuclei. Second, subsignals composing non-dot-like signals are detected and clustered to signals. Features are measured to the signals and a subset of these features is selected representing the signals to a multiclass classifier. Classification using a naive Bayesian classifier (NBC) or a multilayer perceptron is accomplished. When applied to a FISH image database, dot and non-dot-like signals were segmented almost perfectly and then classified with accuracy of approximately 80% by either of the classifiers. |
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