Fast background removal in 3D fluorescence microscopy images using one-class learning

Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang, Danny Z. Chen

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

6 Scopus citations

Abstract

With the recent advances of optical tissue clearing technology, current imaging modalities are able to image large tissue samples in 3D with single-cell resolution. However, the severe background noise remains a significant obstacle to the extraction of quantitative information from these high-resolution 3D images. Additionally, due to the potentially large sizes of 3D image data (over 1011 voxels), the processing speed is becoming a major bottleneck that limits the applicability of many known background correction methods. In this paper, we present a fast background removal algorithm for large volume 3D fluorescence microscopy images. By incorporating unsupervised one-class learning into the percentile filtering approach, our algorithm is able to precisely and efficiently remove background noise even when the sizes and appearances of foreground objects vary greatly. Extensive experiments on real 3D datasets show our method has superior performance and efficiency comparing with the current state-of-the-art background correction method and the rolling ball algorithm in ImageJ.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
EditorsAlejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
PublisherSpringer Verlag
Pages292-299
Number of pages8
ISBN (Print)9783319245737
DOIs
StatePublished - 2015
Externally publishedYes
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9351
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/TerritoryGermany
CityMunich
Period10/5/1510/9/15

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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