A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images

O. McEnery, L. Lucas, YingLiang Ma, PJ Morrow, Christopher Mitchell, Kurt Saetzler

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

1 Citation (Scopus)

Abstract

Analyzing dynamic biological systems, such as blood vessel growth in healing wounds or tumour development, requires high spatial and temporal resolution. Intravital fluorescence microscopy allows for longitudinal subcellular imaging, but it requires the use of advanced image analysis tools in order to quantitatively extract the relevant parameters or the topology of the underlying network structure to subsequently model and simulate such a system mathematically. We will present a fast and robust approach that estimates the vessel diameter with a low coefficient of error < 6% in settings that are typical for such in-vivo imaging scenarios with a low signal-to-noise ratio and often sub-optimal and uneven background illumination. The generated vessel network is geometrically cleansed for an optimal topological representation.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages101-104
Number of pages4
DOIs
Publication statusPublished - 2006
EventBiomedical Imaging: Macro to Nano, 2006. 3rd IEEE International Symposium on -
Duration: 1 Jan 2006 → …

Conference

ConferenceBiomedical Imaging: Macro to Nano, 2006. 3rd IEEE International Symposium on
Period1/01/06 → …

Fingerprint

vessels
microscopy
wound healing
fluorescence
blood vessels
estimates
temporal resolution
image analysis
signal to noise ratios
tumors
topology
spatial resolution
illumination
high resolution
coefficients

Keywords

  • biomedical optical imaging
  • blood vessels
  • fluorescence
  • image representation
  • medical image processing
  • optical microscopy
  • advanced image analysis
  • tools
  • blood vessel growth
  • dynamic biological systems
  • healing wounds
  • high spatial resolution
  • high temporal resolution
  • in-vivo imaging
  • intravital fluorescence microscopy images
  • longitudinal subcellular
  • imaging
  • low signal-to-noise ratio
  • optimal topological representation
  • suboptimal background illumination
  • tumour development
  • uneven background
  • illumination
  • vessel diameter estimation

Cite this

McEnery, O. ; Lucas, L. ; Ma, YingLiang ; Morrow, PJ ; Mitchell, Christopher ; Saetzler, Kurt. / A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images. Unknown Host Publication. 2006. pp. 101-104
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title = "A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images",
abstract = "Analyzing dynamic biological systems, such as blood vessel growth in healing wounds or tumour development, requires high spatial and temporal resolution. Intravital fluorescence microscopy allows for longitudinal subcellular imaging, but it requires the use of advanced image analysis tools in order to quantitatively extract the relevant parameters or the topology of the underlying network structure to subsequently model and simulate such a system mathematically. We will present a fast and robust approach that estimates the vessel diameter with a low coefficient of error < 6{\%} in settings that are typical for such in-vivo imaging scenarios with a low signal-to-noise ratio and often sub-optimal and uneven background illumination. The generated vessel network is geometrically cleansed for an optimal topological representation.",
keywords = "biomedical optical imaging, blood vessels, fluorescence, image representation, medical image processing, optical microscopy, advanced image analysis, tools, blood vessel growth, dynamic biological systems, healing wounds, high spatial resolution, high temporal resolution, in-vivo imaging, intravital fluorescence microscopy images, longitudinal subcellular, imaging, low signal-to-noise ratio, optimal topological representation, suboptimal background illumination, tumour development, uneven background, illumination, vessel diameter estimation",
author = "O. McEnery and L. Lucas and YingLiang Ma and PJ Morrow and Christopher Mitchell and Kurt Saetzler",
year = "2006",
doi = "10.1109/ISBI.2006.1624862",
language = "English",
pages = "101--104",
booktitle = "Unknown Host Publication",

}

McEnery, O, Lucas, L, Ma, Y, Morrow, PJ, Mitchell, C & Saetzler, K 2006, A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images. in Unknown Host Publication. pp. 101-104, Biomedical Imaging: Macro to Nano, 2006. 3rd IEEE International Symposium on, 1/01/06. https://doi.org/10.1109/ISBI.2006.1624862

A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images. / McEnery, O.; Lucas, L.; Ma, YingLiang; Morrow, PJ; Mitchell, Christopher; Saetzler, Kurt.

Unknown Host Publication. 2006. p. 101-104.

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

TY - GEN

T1 - A fast, robust and scale-independent approach to estimate vessel diameters in intravital fluorescence microscopy images

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AU - Lucas, L.

AU - Ma, YingLiang

AU - Morrow, PJ

AU - Mitchell, Christopher

AU - Saetzler, Kurt

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AB - Analyzing dynamic biological systems, such as blood vessel growth in healing wounds or tumour development, requires high spatial and temporal resolution. Intravital fluorescence microscopy allows for longitudinal subcellular imaging, but it requires the use of advanced image analysis tools in order to quantitatively extract the relevant parameters or the topology of the underlying network structure to subsequently model and simulate such a system mathematically. We will present a fast and robust approach that estimates the vessel diameter with a low coefficient of error < 6% in settings that are typical for such in-vivo imaging scenarios with a low signal-to-noise ratio and often sub-optimal and uneven background illumination. The generated vessel network is geometrically cleansed for an optimal topological representation.

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