Nano1D: An accurate Computer Vision model for segmentation and analysis of low-dimensional objects

Abstract

Abstract Microscopy images are usually analyzed qualitatively or manually and there is a need for autonomous quantitative analysis of objects. In this paper, we present a physics-based computational model for accurate segmentation and geometrical analysis of one-dimensional irregular and deformable objects from microscopy images. This model, named Nano1D, has four steps of preprocessing, segmentation, separating overlapped objects and geometrical measurements. The model is tested on Ag nanowires, and successfully segments and analyzes their geometrical characteristics including length, thickness, perimeter and distributions. The function of the algorithm is not undermined by the size, number, density, orientation and overlapping of objects in images. The main strength of the model is shown to be its ability to segment and analyze overlapping objects successfully with more than 99% accuracy, while current machine learning and computational models suffer from inaccuracy and inability to segment overlapping objects. Nano1D can analyze 1D nanoparticles including nanowires, nanotubes, nanorods in addition to other 1D features of microstructures like microcracks, dislocations etc.