Spatial density estimation based segmentation of super-resolution localization microscopy images

Kuan Chieh Jackie Chen, Ge Yang, Jelena Kovacevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages867-871
Number of pages5
ISBN (Electronic)9781479957514
DOIs
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

  • STORM
  • Super-resolution microscopy
  • fluorescence imaging
  • image segmentation
  • spatial density estimation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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