A multispectral imaging analysis system for early detection of cervical cancer

Qiongshui Wu; Libo Zeng; Hengyu Ke; Hong Zheng; Xijian Gao; Diancheng Wang

doi:10.1117/12.592576

20 April 2005 A multispectral imaging analysis system for early detection of cervical cancer

Qiongshui Wu, Libo Zeng, Hengyu Ke, Hong Zheng, Xijian Gao, Diancheng Wang

Proceedings Volume 5745, Medical Imaging 2005: Physics of Medical Imaging; (2005) https://doi.org/10.1117/12.592576
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Cervical cancer is the second most common cancer among women worldwide. Early detection of cervical cancer is very important for successful treatment and increasing survival. Papanicolaou test is the most popular and effective screening test for cervical cancer, but it is highly subjective and skilled-labor intensive. We report a multispectral imaging microscopic system for Papanicolaou smear analysis for early detection of cervical cancer. Different from traditional color imaging method, we use multispectral imaging techniques for image acquisition, which can simultaneously record spectral and spatial information of a sample. A liquid crystal tunable filter (LCTF) is coupled in the light microscope for fast wavelength selection and a two-dimensional cooled charge-coupled device (CCD) for image capture. In this paper, the multispectral image acquisition method is introduced, including exposure control and spectral calibration, which makes the images not so dependent on imaging devices. In the image segmentation process, an effective algorithm using spectral ratio method is applied for cell nuclei detection. This segmentation method can easily detect the nuclei and diminish the influence of the cytoplasm overlap. Results show that our segmentation is more robust and precise than conventional color imaging method which is heavily dependant on sample staining and illumination conditions while with high speed. Once the nuclei have been segmented, cell features including morphological and textural features are measured. A genetic algorithm is used for feature selection and a support vector machine(SVM)is used for training and classification. This paper is focused on image acquisition and segmentation.

Citation Download Citation

Qiongshui Wu, Libo Zeng, Hengyu Ke, Hong Zheng, Xijian Gao, and Diancheng Wang "A multispectral imaging analysis system for early detection of cervical cancer", Proc. SPIE 5745, Medical Imaging 2005: Physics of Medical Imaging, (20 April 2005); https://doi.org/10.1117/12.592576

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