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Segmentation and Classification of Dot and Non-Dot-Like Fluorescence in situ Hybridization Signals for Automated Detection of Cytogenetic Abnormalities

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3 Author(s)
Boaz Lerner ; Ben-Gurion Univ., Beer Sheva ; Lev Koushnir ; Josepha Yeshaya

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 ~80% by either of the classifiers.

Published in:

IEEE Transactions on Information Technology in Biomedicine  (Volume:11 ,  Issue: 4 )