A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

Xingwei Wang; Yuhua Li; Hong Liu; Shibo Li; Roy R. Zhang; Bin Zheng

doi:10.1117/12.910516

22 February 2012 A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection

Xingwei Wang, Yuhua Li, Hong Liu, Shibo Li, Roy R. Zhang, Bin Zheng

Author Affiliations +

Proceedings Volume 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment; 83181C (2012) https://doi.org/10.1117/12.910516
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Fluorescence in situ hybridization (FISH) technology provides a promising molecular imaging tool to detect cervical cancer. Since manual FISH analysis is difficult, time-consuming, and inconsistent, the automated FISH image scanning systems have been developed. Due to limited focal depth of scanned microscopic image, a FISH-probed specimen needs to be scanned in multiple layers that generate huge image data. To improve diagnostic efficiency of using automated FISH image analysis, we developed a computer-aided detection (CAD) scheme. In this experiment, four pap-smear specimen slides were scanned by a dual-detector fluorescence image scanning system that acquired two spectrum images simultaneously, which represent images of interphase cells and FISH-probed chromosome X. During image scanning, once detecting a cell signal, system captured nine image slides by automatically adjusting optical focus. Based on the sharpness index and maximum intensity measurement, cells and FISH signals distributed in 3-D space were projected into a 2-D con-focal image. CAD scheme was applied to each con-focal image to detect analyzable interphase cells using an adaptive multiple-threshold algorithm and detect FISH-probed signals using a top-hat transform. The ratio of abnormal cells was calculated to detect positive cases. In four scanned specimen slides, CAD generated 1676 con-focal images that depicted analyzable cells. FISH-probed signals were independently detected by our CAD algorithm and an observer. The Kappa coefficients for agreement between CAD and observer ranged from 0.69 to 1.0 in detecting/counting FISH signal spots. The study demonstrated the feasibility of applying automated FISH image and signal analysis to assist cyto-geneticists in detecting cervical cancers.

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Xingwei Wang, Yuhua Li, Hong Liu, Shibo Li, Roy R. Zhang, and Bin Zheng "A feasibility assessment of automated FISH image and signal analysis to assist cervical cancer detection", Proc. SPIE 8318, Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment, 83181C (22 February 2012); https://doi.org/10.1117/12.910516

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KEYWORDS

Signal detection

Image analysis

Computer aided design

Cervical cancer

Cancer

Image segmentation

Luminescence

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