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

Fingerprint

Microscopic examination
Image analysis
Fluorophores
Image segmentation
Image understanding
Fluorescence microscopy
Time series
Image processing
Imaging techniques

Keywords

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

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Chen, K. C. J., Yang, G., & Kovacevic, J. (2014). Spatial density estimation based segmentation of super-resolution localization microscopy images. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 867-871). [7025174] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7025174

Spatial density estimation based segmentation of super-resolution localization microscopy images. / Chen, Kuan Chieh Jackie; Yang, Ge; Kovacevic, Jelena.

2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 867-871 7025174.

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

Chen, KCJ, Yang, G & Kovacevic, J 2014, Spatial density estimation based segmentation of super-resolution localization microscopy images. in 2014 IEEE International Conference on Image Processing, ICIP 2014., 7025174, Institute of Electrical and Electronics Engineers Inc., pp. 867-871. https://doi.org/10.1109/ICIP.2014.7025174
Chen KCJ, Yang G, Kovacevic J. Spatial density estimation based segmentation of super-resolution localization microscopy images. In 2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 867-871. 7025174 https://doi.org/10.1109/ICIP.2014.7025174
Chen, Kuan Chieh Jackie ; Yang, Ge ; Kovacevic, Jelena. / Spatial density estimation based segmentation of super-resolution localization microscopy images. 2014 IEEE International Conference on Image Processing, ICIP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 867-871
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