A harmonic analysis view on neuroscience imaging

Paul Hernandez-Herrera, David Jiménez, Ioannis A. Kakadiaris, Andreas Koutsogiannis, Demetrio Labate, Fernanda Laezza, Manos Papadakis

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

After highlighting some of the current trends in neuroscience imaging, this work studies the approximation errors due to varying directional aliasing, arising when 2D or 3D images are subjected to the action of orthogonal transformations. Such errors are common in 3D images of neurons acquired by confocal microscopes. We also present an algorithm for the construction of synthetic data (computational phantoms) for the validation of algorithms for the morphological reconstruction of neurons. Our approach delivers synthetic data that have a very high degree of fidelity with respect to their ground-truth specifications.

Original languageEnglish (US)
Title of host publicationApplied and Numerical Harmonic Analysis
PublisherSpringer International Publishing
Pages423-450
Number of pages28
Edition9780817683788
DOIs
StatePublished - Jan 1 2013

Publication series

NameApplied and Numerical Harmonic Analysis
Number9780817683788
ISSN (Print)2296-5009
ISSN (Electronic)2296-5017

Fingerprint

Harmonic analysis
Neuroscience
Harmonic Analysis
3D Image
Synthetic Data
Neurons
Neuron
Imaging
Imaging techniques
Orthogonal Transformation
Confocal
Aliasing
Approximation Error
Phantom
Microscope
Fidelity
Microscopes
Specification
Specifications
Trends

Keywords

  • Approximation error
  • Confocal microscopy
  • Dendritic arbor segmentation
  • Directional aliasing
  • Synthetic dendrites
  • Synthetic tubular data

ASJC Scopus subject areas

  • Applied Mathematics

Cite this

Hernandez-Herrera, P., Jiménez, D., Kakadiaris, I. A., Koutsogiannis, A., Labate, D., Laezza, F., & Papadakis, M. (2013). A harmonic analysis view on neuroscience imaging. In Applied and Numerical Harmonic Analysis (9780817683788 ed., pp. 423-450). (Applied and Numerical Harmonic Analysis; No. 9780817683788). Springer International Publishing. https://doi.org/10.1007/978-0-8176-8379-5_21

A harmonic analysis view on neuroscience imaging. / Hernandez-Herrera, Paul; Jiménez, David; Kakadiaris, Ioannis A.; Koutsogiannis, Andreas; Labate, Demetrio; Laezza, Fernanda; Papadakis, Manos.

Applied and Numerical Harmonic Analysis. 9780817683788. ed. Springer International Publishing, 2013. p. 423-450 (Applied and Numerical Harmonic Analysis; No. 9780817683788).

Research output: Chapter in Book/Report/Conference proceedingChapter

Hernandez-Herrera, P, Jiménez, D, Kakadiaris, IA, Koutsogiannis, A, Labate, D, Laezza, F & Papadakis, M 2013, A harmonic analysis view on neuroscience imaging. in Applied and Numerical Harmonic Analysis. 9780817683788 edn, Applied and Numerical Harmonic Analysis, no. 9780817683788, Springer International Publishing, pp. 423-450. https://doi.org/10.1007/978-0-8176-8379-5_21
Hernandez-Herrera P, Jiménez D, Kakadiaris IA, Koutsogiannis A, Labate D, Laezza F et al. A harmonic analysis view on neuroscience imaging. In Applied and Numerical Harmonic Analysis. 9780817683788 ed. Springer International Publishing. 2013. p. 423-450. (Applied and Numerical Harmonic Analysis; 9780817683788). https://doi.org/10.1007/978-0-8176-8379-5_21
Hernandez-Herrera, Paul ; Jiménez, David ; Kakadiaris, Ioannis A. ; Koutsogiannis, Andreas ; Labate, Demetrio ; Laezza, Fernanda ; Papadakis, Manos. / A harmonic analysis view on neuroscience imaging. Applied and Numerical Harmonic Analysis. 9780817683788. ed. Springer International Publishing, 2013. pp. 423-450 (Applied and Numerical Harmonic Analysis; 9780817683788).
@inbook{88e98490251e4902bec74d7ded2161e4,
title = "A harmonic analysis view on neuroscience imaging",
abstract = "After highlighting some of the current trends in neuroscience imaging, this work studies the approximation errors due to varying directional aliasing, arising when 2D or 3D images are subjected to the action of orthogonal transformations. Such errors are common in 3D images of neurons acquired by confocal microscopes. We also present an algorithm for the construction of synthetic data (computational phantoms) for the validation of algorithms for the morphological reconstruction of neurons. Our approach delivers synthetic data that have a very high degree of fidelity with respect to their ground-truth specifications.",
keywords = "Approximation error, Confocal microscopy, Dendritic arbor segmentation, Directional aliasing, Synthetic dendrites, Synthetic tubular data",
author = "Paul Hernandez-Herrera and David Jim{\'e}nez and Kakadiaris, {Ioannis A.} and Andreas Koutsogiannis and Demetrio Labate and Fernanda Laezza and Manos Papadakis",
year = "2013",
month = "1",
day = "1",
doi = "10.1007/978-0-8176-8379-5_21",
language = "English (US)",
series = "Applied and Numerical Harmonic Analysis",
publisher = "Springer International Publishing",
number = "9780817683788",
pages = "423--450",
booktitle = "Applied and Numerical Harmonic Analysis",
edition = "9780817683788",

}

TY - CHAP

T1 - A harmonic analysis view on neuroscience imaging

AU - Hernandez-Herrera, Paul

AU - Jiménez, David

AU - Kakadiaris, Ioannis A.

AU - Koutsogiannis, Andreas

AU - Labate, Demetrio

AU - Laezza, Fernanda

AU - Papadakis, Manos

PY - 2013/1/1

Y1 - 2013/1/1

N2 - After highlighting some of the current trends in neuroscience imaging, this work studies the approximation errors due to varying directional aliasing, arising when 2D or 3D images are subjected to the action of orthogonal transformations. Such errors are common in 3D images of neurons acquired by confocal microscopes. We also present an algorithm for the construction of synthetic data (computational phantoms) for the validation of algorithms for the morphological reconstruction of neurons. Our approach delivers synthetic data that have a very high degree of fidelity with respect to their ground-truth specifications.

AB - After highlighting some of the current trends in neuroscience imaging, this work studies the approximation errors due to varying directional aliasing, arising when 2D or 3D images are subjected to the action of orthogonal transformations. Such errors are common in 3D images of neurons acquired by confocal microscopes. We also present an algorithm for the construction of synthetic data (computational phantoms) for the validation of algorithms for the morphological reconstruction of neurons. Our approach delivers synthetic data that have a very high degree of fidelity with respect to their ground-truth specifications.

KW - Approximation error

KW - Confocal microscopy

KW - Dendritic arbor segmentation

KW - Directional aliasing

KW - Synthetic dendrites

KW - Synthetic tubular data

UR - http://www.scopus.com/inward/record.url?scp=85047417440&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047417440&partnerID=8YFLogxK

U2 - 10.1007/978-0-8176-8379-5_21

DO - 10.1007/978-0-8176-8379-5_21

M3 - Chapter

T3 - Applied and Numerical Harmonic Analysis

SP - 423

EP - 450

BT - Applied and Numerical Harmonic Analysis

PB - Springer International Publishing

ER -