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Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2009 (English)In: Proceedings of the 16th Scandinavian Conference on Image Analysis (SCIA), Berlin: Springer-Verlag , 2009, 169-178 p.Conference paper, Published paper (Refereed)
Abstract [en]

When searching for viruses in an electron microscope thesample grid constitutes an enormous search area. Here, we present methodsfor automating the image acquisition process for an automatic virusdiagnostic application. The methods constitute a multi resolution approachwhere we first identify the grid squares and rate individual gridsquares based on content in a grid overview image and then detect regionsof interest in higher resolution images of good grid squares. Our methodsare designed to mimic the actions of a virus TEM expert manually navigatingthe microscope and they are also compared to the expert’s performance.Integrating the proposed methods with the microscope wouldreduce the search area by more than 99.99% and it would also removethe need for an expert to perform the virus search by the microscope.

Place, publisher, year, edition, pages
Berlin: Springer-Verlag , 2009. 169-178 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5575
Keyword [en]
TEM, virus diagnostics, automatic image acquisition
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:uu:diva-108568DOI: 10.1007/978-3-642-02230-2_18ISBN: 978-3-642-02229-6 (print)OAI: oai:DiVA.org:uu-108568DiVA: diva2:236392
Available from: 2009-09-22 Created: 2009-09-22 Last updated: 2014-04-29
In thesis
1. Automatic Virus Identification using TEM: Image Segmentation and Texture Analysis
Open this publication in new window or tab >>Automatic Virus Identification using TEM: Image Segmentation and Texture Analysis
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Automatisk identifiering av virus med hjälp av transmissionselektronmikroskopi : bildsegmentering och texturanalys
Abstract [en]

Viruses and their morphology have been detected and studied with electron microscopy (EM) since the end of the 1930s. The technique has been vital for the discovery of new viruses and in establishing the virus taxonomy. Today, electron microscopy is an important technique in clinical diagnostics. It both serves as a routine diagnostic technique as well as an essential tool for detecting infectious agents in new and unusual disease outbreaks.

The technique does not depend on virus specific targets and can therefore detect any virus present in the sample. New or reemerging viruses can be detected in EM images while being unrecognizable by molecular methods.

One problem with diagnostic EM is its high dependency on experts performing the analysis. Another problematic circumstance is that the EM facilities capable of handling the most dangerous pathogens are few, and decreasing in number.

This thesis addresses these shortcomings with diagnostic EM by proposing image analysis methods mimicking the actions of an expert operating the microscope. The methods cover strategies for automatic image acquisition, segmentation of possible virus particles, as well as methods for extracting characteristic properties from the particles enabling virus identification.

One discriminative property of viruses is their surface morphology or texture in the EM images. Describing texture in digital images is an important part of this thesis. Viruses show up in an arbitrary orientation in the TEM images, making rotation invariant texture description important. Rotation invariance and noise robustness are evaluated for several texture descriptors in the thesis. Three new texture datasets are introduced to facilitate these evaluations. Invariant features and generalization performance in texture recognition are also addressed in a more general context.

The work presented in this thesis has been part of the project Panvirshield, aiming for an automatic diagnostic system for viral pathogens using EM. The work is also part of the miniTEM project where a new desktop low-voltage electron microscope is developed with the aspiration to become an easy to use system reaching high levels of automation for clinical tissue sections, viruses and other nano-sized particles.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 111 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1122
Keyword
image analysis, image processing, virus identification, transmission electron microscopy, texture analysis, texture descriptors
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-217328 (URN)978-91-554-8873-4 (ISBN)
Public defence
2014-03-21, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
Opponent
Supervisors
Available from: 2014-02-28 Created: 2014-02-02 Last updated: 2014-07-21

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Kylberg, Gustaf

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