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Virus recognition based on local texture
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0002-0055-4412
2014 (English)In: Proceedings 22nd International Conference on Pattern Recognition (ICPR), 2014, 2014, 3227-3232 p.Conference paper, Published paper (Refereed)
Abstract [en]

To detect and identify viruses in electron microscopy images is crucial in certain clinical emergency situations. It is currently a highly manual task, requiring an expert sittingat the microscope to perform the analysis visually. Here wefocus on and investigate one aspect towards automating the virusdiagnostic task, namely recognizing the virus type based on theirtexture once possible virus objects have been segmented. Weshow that by using only local texture descriptors we achievea classification rate of almost 89% on texture patches from 15different virus types and a debris (false object) class. We compareand combine 5 different types of local texture descriptors andshow that by combining the different types a lower classificationerror is achieved. We use a Random Forest Classifier and comparetwo approaches for feature selection.

Place, publisher, year, edition, pages
2014. 3227-3232 p.
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-216290DOI: 10.1109/ICPR.2014.556ISI: 000359818003060ISBN: 978-1-4799-5208-3 (print)OAI: oai:DiVA.org:uu-216290DiVA: diva2:692853
Conference
IEEE 22nd International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden
Available from: 2014-02-02 Created: 2014-01-20 Last updated: 2015-10-05Bibliographically approved
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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Sintorn, Ida-MariaKylberg, Gustaf

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