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Contributions to 3D Image Analysis using Discrete Methods and Fuzzy Techniques: With Focus on Images from Cryo-Electron Tomography
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
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 [en]
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: urn:nbn:se:uu:diva-121579ISBN: 978-91-554-7768-4 (print)OAI: oai:DiVA.org:uu-121579DiVA: diva2:305804
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
List of papers
1. Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
Open this publication in new window or tab >>Clustering of Objects in 3D Electron Tomography Reconstructions of Protein Solutions Based on Shape Measurements
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, Published 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.

Keyword
clustering, shape, electron tomography, protein
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-74174 (URN)doi:10.1007/11552499_43 (DOI)3-540-28833-3 (ISBN)
Available from: 2005-10-05 Created: 2005-10-05 Last updated: 2010-03-25
2. Separation of blob-like structures using fuzzy distance based hierarchical clustering
Open this publication in new window or tab >>Separation of blob-like structures using fuzzy distance based hierarchical clustering
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.

National Category
Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-21703 (URN)
Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
3. Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
Open this publication in new window or tab >>Fuzzy Distance Based Hierarchical Clustering Calculated Using the A* Algorithm
2006 (English)In: Combinatorial Image Analysis: 11th International Workshop, IWCIA 2006, Berlin, Germany, June 19-21, 2006, Proceedings, 2006, 101-115 p.Conference paper, Published paper (Refereed)
Abstract [en]

We present a method for calculating fuzzy distances between pairs of points in an image using the A* algorithm and, furthermore, apply this method for fuzzy distance based hierarchical clustering. The method is general and can be of use in numerous applications. In our case we intend to use the clustering in an algorithm for delineation of objects corresponding to parts of proteins in 3D images. The image is defined as a fuzzy object and represented as a graph, enabling a path finding approach for distance calculations. The fuzzy distance between two adjacent points is used as edge weight and a heuristic is defined for fuzzy sets. A* is applied to the calculation of fuzzy distance between pair of points and hierarchical clustering is used to group the points. The normalised Hubert's statistic is used as validity index to determine the number of clusters. The method is tested on three 2D images; two synthetic images and one fuzzy distance transformed microscopy image of stem cells. All experiments show promising initial results.

Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 4040
National Category
Engineering and Technology
Identifiers
urn:nbn:se:uu:diva-21701 (URN)10.1007/11774938_9 (DOI)3-540-35153-1 (ISBN)
Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
4. An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
Open this publication in new window or tab >>An Objective Comparison between Gray Weighted Distance Transforms and Weighted Distance Transforms On Curved Spaces
2006 (English)In: Discrete Geometry for Computer Imagery: 13th International Conference, DGCI 2006, Szeged, Hungary, October 25-27, 2006, Proceedings, 2006, 259-270 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we compare two different definitions of distance transform for gray level images: the Gray Weighted Distance Transform (GWDT), and the Weighted Distance Transform On Curved Space (WDTOCS). We show through theoretical and experimental comparisons the differences, the strengths and the weaknesses of these two distances.

Series
Lecture notes in computer science, ISSN 0302-9743 ; 4245
National Category
Computer and Information Science
Identifiers
urn:nbn:se:uu:diva-21702 (URN)10.1007/11907350_22 (DOI)978-3-540-47651-1 (ISBN)
Available from: 2007-01-03 Created: 2007-01-03 Last updated: 2010-03-25Bibliographically approved
5. Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
Open this publication in new window or tab >>Flexibility Description of the MET Protein Stalk Based on the Use of Non-Uniform B-Splines
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.

Keyword
Cryo-ET, fuzzy distance, geometrical feature extraction
Identifiers
urn:nbn:se:uu:diva-11902 (URN)doi:10.1007/978-3-540-74272-2_22 (DOI)978-3-540-74271-5 (ISBN)
Available from: 2007-11-05 Created: 2007-11-05 Last updated: 2010-03-25
6. Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
Open this publication in new window or tab >>Three-Dimensional Tracing of Neurites in Fluorescence Microscopy Images Using Local Path-Finding
2010 (English)In: 2010 IEEE International Conference On Acoustics, Speech And Signal Processing, 2010. ICASSP 2010, 2010, 646-649 p.Conference paper, Published paper (Refereed)
Abstract [en]

Neurite tracing in 3D neuron images is important when it comes to analysing the growth and functionality of nerve cells. The methods used today are either of high complexity, limiting throughput, or semi-automatic, i.e., requiring user interaction. This makes them unsuitable for analysis where high throughput is needed. In this work we propose a method designed for low complexity and void of user interaction by using local path-finding. The method is illustrated on both phantom and real data, and compared with a widely used commercial software package with promising results.

Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword
Tracing, neurite, path-finding, 3D
National Category
Computer and Information Science
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-121571 (URN)000287096000158 ()978-1-4244-4296-6 (ISBN)
Conference
2010 IEEE International Conference on Acoustics, Speech, and Signal Processing Dallas, TX, MAR 14-19, 2010
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2011-04-18Bibliographically approved
7. Heuristics for grey-weighted distance computations
Open this publication in new window or tab >>Heuristics for grey-weighted distance computations
2010 (English)In: Symposium on Image Analysis, Uppsala, March 11-12. Proceedings SSBA 2010., 2010Conference paper, Published paper (Other academic)
Abstract [en]

With new imaging techniques and computing power come increasing demands on the computation cost of image analysis algorithms. Grey-weighted distance transforms are traditionally computed using graph-based region-growing algorithms. These algorithms expand nodes in all directions by evolving an isotropic wave front. When calculating point-to-point distances, the isotropic propagation can be unnecessarily costly since it expands many nodes in directions away from the goal node. By introducing a heuristic to guide the search, the number of excessive nodes can be decreased. Here we introduce heuristics for computing Distance on Curved Spaces and Weighted Distance on Curved Spaces. We also examine the impact of these heuristics together with a heuristic previously proposed for fuzzy distance computations. The results show that the number of nodes expanded in point-to-point grey-weighted distances can be decreased by up to ~79%.

National Category
Computer and Information Science
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-121572 (URN)
Conference
SSBA 2010
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2011-03-11Bibliographically approved
8. Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
Open this publication in new window or tab >>Image Processing System for Localising Macromolecules in Cryo-Electron Tomography
(English)Manuscript (preprint) (Other academic)
Abstract [en]

A major challenge in today's molecular biology research is to understand the interaction between proteins at the molecular level. Cryo-electron tomography (ET) has come to play an important role in facilitating objective qualitative experiments on protein structures and their interaction. Various protein conformation structures can be qualitatively analysed as complete galleries of proteins are captured by ET. To facilitate fast and objective macromolecular structure analysis procedures, image processing has become a crucial tool. This paper presents an image processing system for localising individual proteins from in vitro samples imaged by ET. We have evaluated the system using simulated data as well as experimental data.

Keyword
Fuzzy set, watershed segmentation, distance transform, proteins
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-121573 (URN)
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25
9. Algorithms for Grey-Weighted Distance Computations
Open this publication in new window or tab >>Algorithms for Grey-Weighted Distance Computations
(English)Manuscript (preprint) (Other academic)
Abstract [en]

With the increasing size of datasets and demand for real time response for interactive applications, improving runtime for algorithms with excessive computational requirements has become increasingly important. Many different algorithms combining efficient priority queues with various helper structures have been proposed for computing grey-weighted distance transforms. Here we compare the performance of popular competitive algorithms in different scenarios to form practical guidelines easy to adopt. The label-setting category of algorithms is shown to be the best choice for all scenarios. The hierarchical heap with a pointer array to keep track of nodes on the heap is shown to be the best choice as priority queue. However, if memory is a critical issue, then the best choice is the Dial priority queue for integer valued costs and the Untidy priority queue for real valued costs.

Keyword
Grey-weighted distance, Geodesic time, Geodesic distance, Fuzzy distance, Algorithms, Region growing
Research subject
Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-121578 (URN)
Available from: 2010-03-25 Created: 2010-03-25 Last updated: 2010-03-25

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  • ieee
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  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
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