Automated Nuclear Lamina Network Recognition and Quantitative Analysis in Structured Illumination Super-Resolution Microscope Images Using a Gaussian Mixture Model and Morphological Processing

Abstract

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.

Keywords

• nuclear lamina network
• structured illumination super-resolution microscope
• image segmentation