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

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

doi:10.1007/978-3-319-24574-4_35

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 proceeding › Conference contribution

7 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 language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings
Editors	Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, William M. Wells
Publisher	Springer Verlag
Pages	292-299
Number of pages	8
ISBN (Print)	9783319245737
DOIs	https://doi.org/10.1007/978-3-319-24574-4_35
State	Published - 2015
Externally published	Yes
Event	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: Oct 5 2015 → Oct 9 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9351
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/Territory	Germany
City	Munich
Period	10/5/15 → 10/9/15

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-24574-4_35

Cite this

Yang, L., Zhang, Y., Guldner, I. H., Zhang, S., & Chen, D. Z. (2015). Fast background removal in 3D fluorescence microscopy images using one-class learning. In A. F. Frangi, N. Navab, J. Hornegger, & W. M. Wells (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings (pp. 292-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351). Springer Verlag. https://doi.org/10.1007/978-3-319-24574-4_35

Fast background removal in 3D fluorescence microscopy images using one-class learning. / Yang, Lin; Zhang, Yizhe; Guldner, Ian H. et al.
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. ed. / Alejandro F. Frangi; Nassir Navab; Joachim Hornegger; William M. Wells. Springer Verlag, 2015. p. 292-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9351).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yang, L, Zhang, Y, Guldner, IH, Zhang, S & Chen, DZ 2015, Fast background removal in 3D fluorescence microscopy images using one-class learning. in AF Frangi, N Navab, J Hornegger & WM Wells (eds), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, Springer Verlag, pp. 292-299, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 10/5/15. https://doi.org/10.1007/978-3-319-24574-4_35

Yang L, Zhang Y, Guldner IH, Zhang S, Chen DZ. Fast background removal in 3D fluorescence microscopy images using one-class learning. In Frangi AF, Navab N, Hornegger J, Wells WM, editors, Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. Springer Verlag. 2015. p. 292-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24574-4_35

Yang, Lin ; Zhang, Yizhe ; Guldner, Ian H. et al. / Fast background removal in 3D fluorescence microscopy images using one-class learning. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 - 18th International Conference, Proceedings. editor / Alejandro F. Frangi ; Nassir Navab ; Joachim Hornegger ; William M. Wells. Springer Verlag, 2015. pp. 292-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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N2 - 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.

AB - 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.

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

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