Segmentation of Partially Overlapping Nanoparticles Using Concave Points

This paper presents a novel method for the segmentation of partially overlapping nanoparticles with a convex shape in silhouette images. The proposed method involves two main steps: contour evidence extraction and contour estimation. Contour evidence extraction starts with contour segmentation where contour segments are recovered from a binarized image by detecting concave points. After this, contour segments which belong to the same object are grouped by utilizing properties of fitted ellipses. Finally, the contour estimation is implemented through a non-linear ellipse fitting problem in which partially observed objects are modeled in the form of ellipse-shape objects. The experiments on a dataset consisting of nanoparticles demonstrate that the proposed method outperforms two current state-of-art approaches in overlapping nanoparticles segmentation. The method relies only on edge information and can be applied to any segmentation problems where the objects are partially overlapping and have an approximately elliptical shape, such as cell segmentation.