Automated Nuclear Lamina Network Recognition and Quantitative Analysis in Structured Illumination Super-Resolution Microscope Images Using a Gaussian Mixture Model and Morphological Processing
Yiwei Chen,
Zhenglong Sun,
Yi He,
Xin Zhang,
Jing Wang,
Wanyue Li,
Lina Xing,
Feng Gao,
Guohua Shi
Affiliations
Yiwei Chen
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Zhenglong Sun
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Yi He
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Xin Zhang
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Jing Wang
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Wanyue Li
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Lina Xing
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Feng Gao
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Guohua Shi
Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
Studying the architecture of nuclear lamina networks is significantly important in biomedicine owing not only to their influence on the genome, but also because they are associated with several diseases. To save labor and time, an automated method for nuclear lamina network recognition and quantitative analysis is proposed for use with lattice structured illumination super-resolution microscope images in this study. This method is based on a Gaussian mixture model and morphological processing. It includes steps for target region generation, bias field correction, image segmentation, network connection, meshwork generation, and meshwork analysis. The effectiveness of the proposed method was confirmed by recognizing and quantitatively analyzing nuclear lamina networks in five images that are presented to show the method’s performance. The experimental results show that our algorithm achieved high accuracy in nuclear lamina network recognition and quantitative analysis, and the median face areas size of lamina networks from U2OS osteosarcoma cells are 0.3184 μm2.
