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Separation of blob-like structures using fuzzy distance based hierarchical clustering
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for 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, Centre for Image Analysis.
2006 (English)In: Symposium on Image Analysis: SSBA 2006, Umeå, Sweden, March 16-17, 2006, Proceedings, 2006Conference paper, Published paper (Other academic)
Abstract [en]

We present a method for separation of clustered biomedical blob-like structures in 2D and 3D images. The local maxima found on the fuzzy distance transform of the image are grouped by fuzzy distance based hierarchical clustering and used as seeds in a seeded watershed segmentation to delineate each individual blob. The method shows good initial results and is illustrated on two types of images: bright field microscopy (2D) images of in vitro stem cells and cryo electron tomography (3D) images of the antibody immunoglobulin G.

Place, publisher, year, edition, pages
2006.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-21703OAI: oai:DiVA.org:uu-21703DiVA: diva2:49476
Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
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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Gedda, MagnusSvensson, Stina

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