Symmetry-based detection of nuclei in microscopy images

Sundaresh Ram, Jeffrey J Rodriguez

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

5 Citations (Scopus)

Abstract

Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages1128-1132
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Fingerprint

Microscopic examination
Cells
Image denoising
Confocal microscopy
Image analysis
Seed
Fluorescence
Imaging techniques

Keywords

  • FISH images
  • FRST
  • Nucleus detection
  • unimodal thresholding

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Ram, S., & Rodriguez, J. J. (2013). Symmetry-based detection of nuclei in microscopy images. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 1128-1132). [6637826] https://doi.org/10.1109/ICASSP.2013.6637826

Symmetry-based detection of nuclei in microscopy images. / Ram, Sundaresh; Rodriguez, Jeffrey J.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 1128-1132 6637826.

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

Ram, S & Rodriguez, JJ 2013, Symmetry-based detection of nuclei in microscopy images. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings., 6637826, pp. 1128-1132, 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, 5/26/13. https://doi.org/10.1109/ICASSP.2013.6637826
Ram S, Rodriguez JJ. Symmetry-based detection of nuclei in microscopy images. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. p. 1128-1132. 6637826 https://doi.org/10.1109/ICASSP.2013.6637826
Ram, Sundaresh ; Rodriguez, Jeffrey J. / Symmetry-based detection of nuclei in microscopy images. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. 2013. pp. 1128-1132
@inproceedings{5f2eb4b4201f43f088f800aaa0b12e19,
title = "Symmetry-based detection of nuclei in microscopy images",
abstract = "Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.",
keywords = "FISH images, FRST, Nucleus detection, unimodal thresholding",
author = "Sundaresh Ram and Rodriguez, {Jeffrey J}",
year = "2013",
month = "10",
day = "18",
doi = "10.1109/ICASSP.2013.6637826",
language = "English (US)",
isbn = "9781479903566",
pages = "1128--1132",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

T1 - Symmetry-based detection of nuclei in microscopy images

AU - Ram, Sundaresh

AU - Rodriguez, Jeffrey J

PY - 2013/10/18

Y1 - 2013/10/18

N2 - Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.

AB - Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.

KW - FISH images

KW - FRST

KW - Nucleus detection

KW - unimodal thresholding

UR - http://www.scopus.com/inward/record.url?scp=84890526385&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84890526385&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2013.6637826

DO - 10.1109/ICASSP.2013.6637826

M3 - Conference contribution

AN - SCOPUS:84890526385

SN - 9781479903566

SP - 1128

EP - 1132

BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ER -