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Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
Uppsala University, Interfaculty Units, Centre for Image Analysis. Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2007 (English)In: 12th International Conference on Computer Analysis of Images and Patterns, 2007, 173-180 p.Conference paper, Published paper (Refereed)
Abstract [en]

The MET protein controls growth, invasion, and metastasis in cancer cells and is thereby of interest to study, for example from a structural point of view. For individual particle imaging by Cryo-Electron Tomography of the MET protein, or other proteins, dedicated image analysis methods are required to extract information in a robust way as the images have low contrast and resolution (with respect to the size of the imaged structure). We present a method to identify the two parts of the MET protein, Beta-propeller and stalk, using a fuzzy framework. Furthermore, we describe how a representation of the MET stalk, denoted stalk curve, can be identified based on the use of non-uniform B-splines. The stalk curve is used to extract relevant geometrical information about the stalk, e.g., to facilitate curvature and length measurements.

Place, publisher, year, edition, pages
2007. 173-180 p.
Keyword [en]
Cryo-ET, fuzzy distance, geometrical feature extraction
Identifiers
URN: urn:nbn:se:uu:diva-11902DOI: doi:10.1007/978-3-540-74272-2_22ISBN: 978-3-540-74271-5 (print)OAI: oai:DiVA.org:uu-11902DiVA: diva2:39671
Available from: 2007-11-05 Created: 2007-11-05 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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 727
Keyword
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
Identifiers
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)
Opponent
Supervisors
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/10.1007/978-3-540-74272-2_22

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Gedda, MagnusSvensson, Stina

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