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Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2005 (English)In: Pattern Recognition and Image Analysis: Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 2005, Proceedings, Part II, 2005, 809- p.Conference paper (Refereed)
Abstract [en]

This paper evaluates whether shape features can be used for clustering objects in Sidec (tm), Electron Tomography (SET) reconstructions. SET reconstructions contain a large number of objects, and only a few of them are of interest. It is desired to limit the analysis to contain as few uninteresting objects as possible. Unsupervised hierarchical clustering is used to group objects into classes. Experiments are done on one synthetic data set and two data sets from a SET reconstruction of a human growth hormone (1hwg) in solution. The experiments indicate that clustering of objects in SET reconstructions based on shape features is useful for finding structural classes.

Place, publisher, year, edition, pages
2005. 809- p.
Keyword [en]
clustering, shape, electron tomography, protein
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:uu:diva-74174DOI: doi:10.1007/11552499_43ISBN: 3-540-28833-3OAI: oai:DiVA.org:uu-74174DiVA: diva2:102084
Available from: 2005-10-05 Created: 2005-10-05 Last updated: 2010-03-25
In thesis
1. Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography
Open this publication in new window or tab >>Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With the emergence of new imaging techniques, researchers are always eager to push the boundaries by examining objects either smaller or further away than what was previously possible. The development of image analysis techniques has greatly helped to introduce objectivity and coherence in measurements and decision making. It has become an essential tool for facilitating both large-scale quantitative studies and qualitative research. In this Thesis, methods were developed for analysis of low-resolution (in respect to the size of the imaged objects) three-dimensional (3D) images with low signal-to-noise ratios (SNR) applied to images from cryo-electron tomography (cryo-ET) and fluorescence microscopy (FM). The main focus is on methods of low complexity, that take into account both grey-level and shape information, to facilitate large-scale studies. Methods were developed to localise and represent complex macromolecules in images from cryo-ET. The methods were applied to Immunoglobulin G (IgG) antibodies and MET proteins. The low resolution and low SNR required that grey-level information was utilised to create fuzzy representations of the macromolecules. To extract structural properties, a method was developed to use grey-level-based distance measures to facilitate decomposition of the fuzzy representations into sub-domains. The structural properties of the MET protein were analysed by developing a analytical curve representation of its stalk. To facilitate large-scale analysis of structural properties of nerve cells, a method for tracing neurites in FM images using local path-finding was developed. Both theoretical and implementational details of computationally heavy approaches were examined to keep the time complexity low in the developed methods. Grey-weighted distance definitions and various aspects of their implementations were examined in detail to form guidelines on which definition to use in which setting and which implementation is the fastest. Heuristics were developed to speed up computations when calculating grey-weighted distances between two points. The methods were evaluated on both real and synthetic data and the results show that the methods provide a step towards facilitating large-scale studies of images from both cryo-ET and FM.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. 70 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 727
digital image analysis, 3D, fuzzy, algorithms, grey-weighted distance, region growing, electron tomography, tracing, fluorescence microscopy
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
urn:nbn:se:uu:diva-121579 (URN)978-91-554-7768-4 (ISBN)
Public defence
2010-05-20, Polhemsalen, Lägerhyddsvägen 1, Uppsala, 10:15 (English)
Available from: 2010-04-22 Created: 2010-03-25 Last updated: 2015-01-23Bibliographically approved

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Publisher's full texthttp://dx.doi.org/10.1007/11552499_43

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Gedda, Magnus
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