Date of Original Version
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Abstract or Description
Super-resolution localization microscopy (SRLM) is a new imaging modality that is capable of resolving cellular structures at nanometer resolution, providing unprecedented insight into biological processes. Each SRLM image is reconstructed from a time series of images of randomly activated fluorophores that are localized at nanometer resolution and represented by clusters of particles of varying spatial densities. SRLM images differ significantly from conventional fluorescence microscopy images because of fundamental differences in image formation. Currently, however, quantitative image analysis techniques developed or optimized specifically for SRLM images are lacking, which significantly limit accurate and reliable image analysis. This is especially the case for image segmentation, an essential operation for image analysis and understanding. In this study, we propose a simple SRLM image segmentation technique based on estimating and smoothing spatial densities of fluorophores using adaptive anisotropic kernels. Experimental results showed that the proposed method provided robust and accurate segmentation of SRLM images and significantly outperformed conventional segmentation approaches such as active contour methods in segmentation accuracy.
Proceedings of the IEEE International Conference on Image Processing (ICIP), 2014, 867-871.