Segmentation of microspheres in ultrahigh density multiplexed microsphere-based assays

Abhishek Mathur; David M Kelso

doi:10.1117/12.643170

Segmentation of microspheres in ultrahigh density multiplexed microsphere-based assays

Abhishek Mathur^*, David M Kelso

^*Corresponding author for this work

Biomedical Engineering

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

1 Scopus citations

Abstract

We have developed a method to identify and localize luminescent microspheres in dense images of microsphere-based assays. Application of this algorithm to the images of densely packed microspheres would aid in increasing the number of assays per unit target sample volume by several orders of magnitude. We immobilize or sediment microspheres on microscope slides and read luminescence from these randomly arrayed microspheres with a digital imaging microscope equipped with a cooled CCD camera. Our segmentation algorithm, which is based on marker-controlled watershed transformation, is then implemented to segment the microsphere clusters in the luminescent images acquired at different wavelengths. This segmentation algorithm is fully automated and require no manual intervention or training sets for optimizing the parameters and is much more accurate than previously proposed algorithms. Using this algorithm, we have accurately segmented more than 97% of the microspheres in dense images.

Original language	English (US)
Title of host publication	Image Processing
Subtitle of host publication	Algorithms and Systems, Neural Networks, and Machine Learning - Proceedings of SPIE-IS and T Electronic Imaging
Volume	6064
DOIs	https://doi.org/10.1117/12.643170
State	Published - Apr 17 2006
Event	Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning - San Jose, CA, United States Duration: Jan 16 2006 → Jan 18 2006

Other

Other	Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning
Country	United States
City	San Jose, CA
Period	1/16/06 → 1/18/06

Keywords

Cluster segmentation
Fully automated
Image analysis
Microsphere-based assays
Watershed

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.643170

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Cite this

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title = "Segmentation of microspheres in ultrahigh density multiplexed microsphere-based assays",

abstract = "We have developed a method to identify and localize luminescent microspheres in dense images of microsphere-based assays. Application of this algorithm to the images of densely packed microspheres would aid in increasing the number of assays per unit target sample volume by several orders of magnitude. We immobilize or sediment microspheres on microscope slides and read luminescence from these randomly arrayed microspheres with a digital imaging microscope equipped with a cooled CCD camera. Our segmentation algorithm, which is based on marker-controlled watershed transformation, is then implemented to segment the microsphere clusters in the luminescent images acquired at different wavelengths. This segmentation algorithm is fully automated and require no manual intervention or training sets for optimizing the parameters and is much more accurate than previously proposed algorithms. Using this algorithm, we have accurately segmented more than 97% of the microspheres in dense images.",

keywords = "Cluster segmentation, Fully automated, Image analysis, Microsphere-based assays, Watershed",

author = "Abhishek Mathur and Kelso, {David M}",

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}

Mathur, A & Kelso, DM 2006, Segmentation of microspheres in ultrahigh density multiplexed microsphere-based assays. in Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning - Proceedings of SPIE-IS and T Electronic Imaging. vol. 6064, 60640B, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, San Jose, CA, United States, 1/16/06. https://doi.org/10.1117/12.643170

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AU - Kelso, David M

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N2 - We have developed a method to identify and localize luminescent microspheres in dense images of microsphere-based assays. Application of this algorithm to the images of densely packed microspheres would aid in increasing the number of assays per unit target sample volume by several orders of magnitude. We immobilize or sediment microspheres on microscope slides and read luminescence from these randomly arrayed microspheres with a digital imaging microscope equipped with a cooled CCD camera. Our segmentation algorithm, which is based on marker-controlled watershed transformation, is then implemented to segment the microsphere clusters in the luminescent images acquired at different wavelengths. This segmentation algorithm is fully automated and require no manual intervention or training sets for optimizing the parameters and is much more accurate than previously proposed algorithms. Using this algorithm, we have accurately segmented more than 97% of the microspheres in dense images.

AB - We have developed a method to identify and localize luminescent microspheres in dense images of microsphere-based assays. Application of this algorithm to the images of densely packed microspheres would aid in increasing the number of assays per unit target sample volume by several orders of magnitude. We immobilize or sediment microspheres on microscope slides and read luminescence from these randomly arrayed microspheres with a digital imaging microscope equipped with a cooled CCD camera. Our segmentation algorithm, which is based on marker-controlled watershed transformation, is then implemented to segment the microsphere clusters in the luminescent images acquired at different wavelengths. This segmentation algorithm is fully automated and require no manual intervention or training sets for optimizing the parameters and is much more accurate than previously proposed algorithms. Using this algorithm, we have accurately segmented more than 97% of the microspheres in dense images.

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KW - Fully automated

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