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Automated feature detection in multidimensional images: a unified tensor approach
Linköping University, Department of Biomedical Engineering, Biomedical Modelling and Simulation. Linköping University, The Institute of Technology.
2001 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Manual identification of structures and features in multidimensional images is at best time consuming and operator dependent. Feature identification need to be accurate, repeatable and quantitative. This thesis presents a unified approach for automatic feature detection in multidimensional scalar and vector fields. The basis for the feature detection is a tensor representation of multidimensional local neighborhoods, constructed from a filter response controlled linear combination of basis tensors. Using eigenvalue and eigenvector decomposition, local topology and local orientation can be estimated. With different filter sets the tensor representation can be used to find specific features in multidimensional images, such as planar structures in scalar fields or flow structures as vortices or parallel flow in vector fields.

The motivation for the unified approach for feature detection in both scalar and vector fields is to build a foundation to tackle the challenge of understanding the complex interaction between the cardiac walls and the blood flow in the human heart.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet , 2001. , 42 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 909
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-33625Local ID: LiU-TEK-LIC-2001:46ISBN: 91-7373-131-5 (print)OAI: oai:DiVA.org:liu-33625DiVA: diva2:254448
Presentation
2001-11-30, Terassen, Universitetssjukhuset, Linköping, 10:15 (Swedish)
Opponent
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-07Bibliographically approved
List of papers
1. Segmentation of echo cardiographic image sequences using spatio-temporal information
Open this publication in new window or tab >>Segmentation of echo cardiographic image sequences using spatio-temporal information
1999 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI’99: Second International Conference, Cambridge, UK, September 19-22, 1999. Proceedings / [ed] Chris Taylor, Alan Colchester, Berlin: Springer, 1999, Vol. 1679, 410-419 p.Chapter in book (Refereed)
Abstract [en]

This paper describes a new method for improving border detection in image sequences by including both spatial and temporal information. The method is based on three dimensional quadrature filters for estimating local orientation. A simplification that gives a significant reduction in computational demand is also presented. The border detection framework is combined with a segmentation algorithm based on active contours or ’snakes’, implemented using a new optimization relaxation that can be solved to optimality using dynamical programming. The aim of the study was to compare segmentation performance using gradient based border detection and the proposed border detection algorithm using spatio-temporal information. Evaluation is performed both on a phantom and in-vivo data from five echocardiographic short axis image sequences. It could be concluded that when temporal information was included weak and incomplete boundaries could be found where gradient based border detection failed. Otherwise there was no significant difference in performance between the new proposed method and gradient based border detection.

Place, publisher, year, edition, pages
Berlin: Springer, 1999
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 1679
Keyword
medical image computing, computer-assisted intervention, data-driven image segmentation, structural models, image processing, feature detection, surfaces, shape, measurement, image interpretation, spatiotemporal analysis, diffusion tensor analysis, image registration, data fusion, data visualisation, image-guided intervention, robotic systems, biomechanics, simulation
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-49138 (URN)10.1007/10704282_45 (DOI)3-540-66503-X (ISBN)
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2014-03-28
2. Three-dimensional flow characterization using vector pattern matching
Open this publication in new window or tab >>Three-dimensional flow characterization using vector pattern matching
2003 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 9, no 3, 313-319 p.Article in journal (Refereed) Published
Abstract [en]

This paper describes a novel method for regional characterization of three-dimensional vector fields using a pattern matching approach. Given a three-dimensional vector field, the goal is to automatically locate, identify, and visualize a selected set of classes of structures or features. Rather than analytically defining the properties that must be fulfilled in a region in order to be classified as a specific structure, a set of idealized patterns for each structure type is constructed. Similarity to these patterns is then defined and calculated. Examples of structures of interest include vortices, swirling flow, diverging or converging flow, and parallel flow. Both medical and aerodynamic applications are presented in this paper.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-26709 (URN)10.1109/TVCG.2003.1207439 (DOI)11302 (Local ID)11302 (Archive number)11302 (OAI)
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
3. Efficient representations in matlab made easy - a tensor array toolbox
Open this publication in new window or tab >>Efficient representations in matlab made easy - a tensor array toolbox
2001 (English)In: Proceedings of Nordic Matlab Conference, 2001, 2001, 213-216 p.Conference paper, Published paper (Refereed)
Abstract [en]

Tensors can be used to create efficient and intuitive representations for a wide variety of applications, including signal and image processing, mechanics and fluid dynamics. In order to achieve this in Matlab, a toolbox was developed designed to enhance Matlab's ability to store and manipulate arrays, such that each element in the array can be vectors or general tensors. This paper describes the implementation of the tool box and gives several examples on the usage of tensor representations for signal and image processing. Furthermore, the representation and processing of uncertain data using tensor representations is described as well.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-27346 (URN)11998 (Local ID)11998 (Archive number)11998 (OAI)
Conference
Nordic Matlab Conference. 17-18 Oct, Oslo, Norway, 2001
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2013-11-07

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Heiberg, Einar

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