Gigabytes to bytes: Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography

Rajesh Narasimha, Adam Bennett, Daniel Zabransky, Rachid Sougrat, Steven McLaughlin, Sriram Subramaniam

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

Abstract

Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medieally relevant objects with sizes in the "nano" range of 10-1000nm. The development of methods to analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment 3D features in cellular tomograms that can work robustly and rapidly despite of low signal to noise ratios inherent to biological electron microscopy. This work evaluates various denoising techniques on tomograms obtained using dual-axis simultaneous iterative reconstruction (SIRT) technique. Using three-dimensional images of HIV in infected human macrophages as an example, we demonstrate that transform domain-denoising techniques significantly improve the fidelity of automated feature extraction. Importantly, our approaches represent an vital step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitate the overall goal of speeding up the process of reducing gigabyte-sized tomograms to byte-sized data.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Fingerprint

human immunodeficiency virus
pattern recognition
Tomography
Feature extraction
tomography
macrophages
Electrons
Macrophages
inference
Electron microscopy
Explosions
data acquisition
explosions
Data acquisition
Signal to noise ratio
electron microscopy
signal to noise ratios
electrons

Keywords

  • Denoising
  • Dual-axis SIRT and automated techniques
  • Electron tomography
  • Feature extraction
  • HIV

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

Cite this

Narasimha, R., Bennett, A., Zabransky, D., Sougrat, R., McLaughlin, S., & Subramaniam, S. (2007). Gigabytes to bytes: Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (Vol. 1). [4217076] https://doi.org/10.1109/ICASSP.2007.366676

Gigabytes to bytes : Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography. / Narasimha, Rajesh; Bennett, Adam; Zabransky, Daniel; Sougrat, Rachid; McLaughlin, Steven; Subramaniam, Sriram.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 1 2007. 4217076.

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

Narasimha, R, Bennett, A, Zabransky, D, Sougrat, R, McLaughlin, S & Subramaniam, S 2007, Gigabytes to bytes: Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography. in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. vol. 1, 4217076, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, Honolulu, HI, United States, 4/15/07. https://doi.org/10.1109/ICASSP.2007.366676
Narasimha R, Bennett A, Zabransky D, Sougrat R, McLaughlin S, Subramaniam S. Gigabytes to bytes: Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 1. 2007. 4217076 https://doi.org/10.1109/ICASSP.2007.366676
Narasimha, Rajesh ; Bennett, Adam ; Zabransky, Daniel ; Sougrat, Rachid ; McLaughlin, Steven ; Subramaniam, Sriram. / Gigabytes to bytes : Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Vol. 1 2007.
@inproceedings{8c33959458024ec2810940161ba5e9b6,
title = "Gigabytes to bytes: Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography",
abstract = "Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medieally relevant objects with sizes in the {"}nano{"} range of 10-1000nm. The development of methods to analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment 3D features in cellular tomograms that can work robustly and rapidly despite of low signal to noise ratios inherent to biological electron microscopy. This work evaluates various denoising techniques on tomograms obtained using dual-axis simultaneous iterative reconstruction (SIRT) technique. Using three-dimensional images of HIV in infected human macrophages as an example, we demonstrate that transform domain-denoising techniques significantly improve the fidelity of automated feature extraction. Importantly, our approaches represent an vital step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitate the overall goal of speeding up the process of reducing gigabyte-sized tomograms to byte-sized data.",
keywords = "Denoising, Dual-axis SIRT and automated techniques, Electron tomography, Feature extraction, HIV",
author = "Rajesh Narasimha and Adam Bennett and Daniel Zabransky and Rachid Sougrat and Steven McLaughlin and Sriram Subramaniam",
year = "2007",
doi = "10.1109/ICASSP.2007.366676",
language = "English (US)",
isbn = "1424407281",
volume = "1",
booktitle = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

}

TY - GEN

T1 - Gigabytes to bytes

T2 - Automated denoising and feature extraction as applied to the analysis of HIV architecture and variability using electron tomography

AU - Narasimha, Rajesh

AU - Bennett, Adam

AU - Zabransky, Daniel

AU - Sougrat, Rachid

AU - McLaughlin, Steven

AU - Subramaniam, Sriram

PY - 2007

Y1 - 2007

N2 - Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medieally relevant objects with sizes in the "nano" range of 10-1000nm. The development of methods to analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment 3D features in cellular tomograms that can work robustly and rapidly despite of low signal to noise ratios inherent to biological electron microscopy. This work evaluates various denoising techniques on tomograms obtained using dual-axis simultaneous iterative reconstruction (SIRT) technique. Using three-dimensional images of HIV in infected human macrophages as an example, we demonstrate that transform domain-denoising techniques significantly improve the fidelity of automated feature extraction. Importantly, our approaches represent an vital step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitate the overall goal of speeding up the process of reducing gigabyte-sized tomograms to byte-sized data.

AB - Advances in automated data acquisition in electron tomography have led to an explosion in the amount of data that can be obtained about the spatial architecture of a variety of biologically and medieally relevant objects with sizes in the "nano" range of 10-1000nm. The development of methods to analyze the vast amounts of information contained in these tomograms is a major challenge since the electron tomograms are intrinsically noisy. A fundamental step in the automatic analysis of large amounts of data for statistical inference is to segment 3D features in cellular tomograms that can work robustly and rapidly despite of low signal to noise ratios inherent to biological electron microscopy. This work evaluates various denoising techniques on tomograms obtained using dual-axis simultaneous iterative reconstruction (SIRT) technique. Using three-dimensional images of HIV in infected human macrophages as an example, we demonstrate that transform domain-denoising techniques significantly improve the fidelity of automated feature extraction. Importantly, our approaches represent an vital step in automating the efficient extraction of useful information from large datasets in biological tomography, and facilitate the overall goal of speeding up the process of reducing gigabyte-sized tomograms to byte-sized data.

KW - Denoising

KW - Dual-axis SIRT and automated techniques

KW - Electron tomography

KW - Feature extraction

KW - HIV

UR - http://www.scopus.com/inward/record.url?scp=34547536650&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34547536650&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2007.366676

DO - 10.1109/ICASSP.2007.366676

M3 - Conference contribution

AN - SCOPUS:34547536650

SN - 1424407281

SN - 9781424407286

VL - 1

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

ER -