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Analytical Cellular Pathology
Volume 35 (2012), Issue 5-6, Pages 395-405

Fluorescence In Situ Hybridization (FISH) Signal Analysis Using Automated Generated Projection Images

Xingwei Wang,1 Xiaodong Chen,2 Yuhua Li,2 Hong Liu,2 Shibo Li,3 Roy R. Zhang,3 and Bin Zheng1

1Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA
2Department of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA
3Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma, OK, USA

Received 14 January 2012; Accepted 24 July 2012

Copyright © 2012 Hindawi Publishing Corporation and the authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Fluorescence in situ hybridization (FISH) tests provide promising molecular imaging biomarkers to more accurately and reliably detect and diagnose cancers and genetic disorders. Since current manual FISH signal analysis is low-efficient and inconsistent, which limits its clinical utility, developing automated FISH image scanning systems and computer-aided detection (CAD) schemes has been attracting research interests. To acquire high-resolution FISH images in a multi-spectral scanning mode, a huge amount of image data with the stack of the multiple three-dimensional (3-D) image slices is generated from a single specimen. Automated preprocessing these scanned images to eliminate the non-useful and redundant data is important to make the automated FISH tests acceptable in clinical applications. In this study, a dual-detector fluorescence image scanning system was applied to scan four specimen slides with FISH-probed chromosome X. A CAD scheme was developed to detect analyzable interphase cells and map the multiple imaging slices recorded FISH-probed signals into the 2-D projection images. CAD scheme was then applied to each projection image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm, identify FISH-probed signals using a top-hat transform, and compute the ratios between the normal and abnormal cells. To assess CAD performance, the FISH-probed signals were also independently visually detected by an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots in four testing samples. The study demonstrated the feasibility of automated FISH signal analysis that applying a CAD scheme to the automated generated 2-D projection images.