Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automatic Virus Identification using TEM: Image Segmentation and Texture Analysis
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)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Automatisk identifiering av virus med hjälp av transmissionselektronmikroskopi : bildsegmentering och texturanalys (Swedish)
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 [en]
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: urn:nbn:se:uu:diva-217328ISBN: 978-91-554-8873-4 (print)OAI: oai:DiVA.org:uu-217328DiVA: diva2:693760
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
List of papers
1. Evaluation of noise robustness for local binary pattern descriptors in texture classification
Open this publication in new window or tab >>Evaluation of noise robustness for local binary pattern descriptors in texture classification
2013 (English)In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281, no 17Article in journal (Refereed) Published
Abstract [en]

Local binary pattern (LBP) operators have become commonly used texture descriptors in recent years. Several new LBP-based descriptors have been proposed, of which some aim at improving robustness to noise. To do this, the thresholding and encoding schemes used in the descriptors are modified. In this article, the robustness to noise for the eight following LBP-based descriptors are evaluated; improved LBP, median binary patterns (MBP), local ternary patterns (LTP), improved LTP (ILTP), local quinary patterns, robust LBP, and fuzzy LBP (FLBP). To put their performance into perspective they are compared to three well-known reference descriptors; the classic LBP, Gabor filter banks (GF), and standard descriptors derived from gray-level co-occurrence matrices. In addition, a roughly five times faster implementation of the FLBP descriptor is presented, and a new descriptor which we call shift LBP is introduced as an even faster approximation to the FLBP. The texture descriptors are compared and evaluated on six texture datasets; Brodatz, KTH-TIPS2b, Kylberg, Mondial Marmi, UIUC, and a Virus texture dataset. After optimizing all parameters for each dataset the descriptors are evaluated under increasing levels of additive Gaussian white noise. The discriminating power of the texture descriptors is assessed using tenfolded cross-validation of a nearest neighbor classifier. The results show that several of the descriptors perform well at low levels of noise while they all suffer, to different degrees, from higher levels of introduced noise. In our tests, ILTP and FLBP show an overall good performance on several datasets. The GF are often very noise robust compared to the LBP-family under moderate to high levels of noise but not necessarily the best descriptor under low levels of added noise. In our tests, MBP is neither a good texture descriptor nor stable to noise.

Place, publisher, year, edition, pages
Springer, 2013
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-203664 (URN)10.1186/1687-5281-2013-17 (DOI)000321866700001 ()
Available from: 2013-07-17 Created: 2013-07-17 Last updated: 2017-12-06Bibliographically approved
2. Regional Zernike Moments for Texture Recognition
Open this publication in new window or tab >>Regional Zernike Moments for Texture Recognition
2012 (English)In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, 1635-1638 p.Conference paper, Published paper (Refereed)
Abstract [en]

 Zernike moments are commonly used in pattern recognition but are not suited for texture analysis. In this paper we introduce regional Zernike moments (RZM) where we combine the Zernike moments for the pixels in a region to create a measure suitable for texture analysis. We compare our proposed measures to texture measures based on Gabor filters, Haralick co-occurrence matrices and local binary patterns on two different texture image sets, and show that they are noise insensitive and very well suited for texture recognition.

Keyword
Statistical, Syntactic and Structural Pattern Recognition, Segmentation, Color and Texture, Classification and Clustering
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-188373 (URN)
Conference
ICPR 2012
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2014-04-29
3. Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
Open this publication in new window or tab >>Comparing Rotation Invariance and Interpolation Methods in Texture Recognition Based on Local Binary Pattern Features
(English)Article in journal (Refereed) Submitted
National Category
Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-217327 (URN)
Available from: 2014-02-02 Created: 2014-02-02 Last updated: 2014-04-29
4. Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
Open this publication in new window or tab >>Exploring Filter Banks Based on Orthogonal Moments for Texture Recognition
(English)Manuscript (preprint) (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-188371 (URN)
Available from: 2014-02-02 Created: 2012-12-17 Last updated: 2014-04-29
5. A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
Open this publication in new window or tab >>A Note on: Invariant Features, Overfitting and Generalization Performance in Texture Recognition
(English)Manuscript (preprint) (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-217326 (URN)
Available from: 2014-02-02 Created: 2014-02-02 Last updated: 2014-04-29
6. Kylberg Texture Dataset v. 1.0
Open this publication in new window or tab >>Kylberg Texture Dataset v. 1.0
2011 (English)Report (Other academic)
Place, publisher, year, edition, pages
Uppsala: Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, 2011. 4 p.
Series
External report (Blue series), 35
Keyword
texture dataset
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-163125 (URN)
Available from: 2011-12-08 Created: 2011-12-08 Last updated: 2014-04-29Bibliographically approved
7. Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
Open this publication in new window or tab >>Towards Automated TEM for Virus Diagnostics: Segmentation of Grid Squares and Detection of Regions of Interest
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
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5575
Keyword
TEM, virus diagnostics, automatic image acquisition
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-108568 (URN)10.1007/978-3-642-02230-2_18 (DOI)978-3-642-02229-6 (ISBN)
Available from: 2009-09-22 Created: 2009-09-22 Last updated: 2014-04-29
8. Segmentation of virus particle candidates in transmission electron microscopy images
Open this publication in new window or tab >>Segmentation of virus particle candidates in transmission electron microscopy images
Show others...
2012 (English)In: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 245, no 2, 140-147 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.

Place, publisher, year, edition, pages
Blackwell Publishing, 2012
Keyword
radial density profile, transmission electron microscopy, virus detection, virus segmentation
National Category
Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-163761 (URN)10.1111/j.1365-2818.2011.03556.x (DOI)000298987100004 ()
Available from: 2011-10-04 Created: 2011-12-14 Last updated: 2017-12-08Bibliographically approved
9. Virus texture analysis using local binary patterns and radial density profiles
Open this publication in new window or tab >>Virus texture analysis using local binary patterns and radial density profiles
2011 (English)In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications / [ed] San Martin, César; Kim, Sang-Woon, Springer Berlin/Heidelberg, 2011, 573-580 p.Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the discriminant power of two local and two global texture measures on virus images. The viruses are imaged using negative stain transmission electron microscopy. Local binary patterns and a multi scale extension are compared to radial density profiles in the spatial domain and in the Fourier domain. To assess the discriminant potential of the texture measures a Random Forest classifier is used. Our analysis shows that the multi scale extension performs better than the standard local binary patterns and that radial density profiles in comparison is a rather poor virus texture discriminating measure. Furthermore, we show that the multi scale extension and the profiles in Fourier domain are both good texture measures and that they complement each other well, that is, they seem to detect different texture properties. Combining the two, hence, improves the discrimination between virus textures.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7042
Keyword
virus morphology, texture analysis, local binary patterns, radial density profiles
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-163119 (URN)10.1007/978-3-642-25085-9_68 (DOI)978-3-642-25084-2 (ISBN)
Conference
16th Iberoamerican Congress on Pattern Recognition
Available from: 2011-12-08 Created: 2011-12-08 Last updated: 2014-04-29Bibliographically approved
10. Virus recognition based on local texture
Open this publication in new window or tab >>Virus recognition based on local texture
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.

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:nbn:se:uu:diva-216290 (URN)10.1109/ICPR.2014.556 (DOI)000359818003060 ()978-1-4799-5208-3 (ISBN)
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

Open Access in DiVA

fulltext(46837 kB)1209 downloads
File information
File name FULLTEXT01.pdfFile size 46837 kBChecksum SHA-512
e34b9b326f69dabe91ad2a707d426282871a6adec47be79b7b12d2aeda073fa5d7b099db104ad253b31e7a92158d71d616daa527d8a5d09457912118470c2af1
Type fulltextMimetype application/pdf
Buy this publication >>

Search in DiVA

By author/editor
Kylberg, Gustaf
By organisation
Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer Interaction
Medical Image Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 1209 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 3809 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf