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Segmentation of echo cardiographic image sequences using spatio-temporal information
Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medicine and Care, Clinical Physiology. Linköping University, Faculty of Health Sciences.
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. Vol. 1679, 410-419 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 1679
Keyword [en]
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: urn:nbn:se:liu:diva-49138DOI: 10.1007/10704282_45ISBN: 3-540-66503-X (print)OAI: oai:DiVA.org:liu-49138DiVA: diva2:270034
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2014-03-28
In thesis
1. Automated feature detection in multidimensional images
Open this publication in new window or tab >>Automated feature detection in multidimensional images
2005 (English)Doctoral 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 novel methods for automated feature detection in multidimensional images that are independent on imaging modality. Feature detection is described at two abstraction levels. At the first low level the image is regionally processed to find local or regional features. In the second medium level results are taken from the low level feature detection and grouped into objects or parts that can be quantified. A key to quantification of cardiac function is delineation of the cardiac walls which is a difficult task. Two different methods are described and evaluated for delineation of the left ventricular wall from anatomical images. The results show that semi-automatic delineation is a huge time saver compared to manual delineation. To obtain a robust results as much a priori and image information as possible should be used in the delineation process. Regional cardiac wall function is further studied by deriving and analyzing strain-rate tensors from velocity encoded images. For flow encoded images novel methods to find regional flow structures such as vortex cores, flow based delineation, and flow quantification are proposed. These methods are applied to study blood flow in the human heart, but the techniques outlined are general and can be applied to a wide array of flow conditions.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2005. 70 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 917
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-29497 (URN)14852 (Local ID)91-85297-10-0 (ISBN)14852 (Archive number)14852 (OAI)
Public defence
2005-04-15, Elsa Brändströmsalen, Campus US, Linköpings Universitet, Linköping, 13:00 (English)
Opponent
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2012-12-10Bibliographically approved
2. Automated feature detection in multidimensional images: a unified tensor approach
Open this publication in new window or tab >>Automated feature detection in multidimensional images: a unified tensor approach
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:nbn:se:liu:diva-33625 (URN)LiU-TEK-LIC-2001:46 (Local ID)91-7373-131-5 (ISBN)LiU-TEK-LIC-2001:46 (Archive number)LiU-TEK-LIC-2001:46 (OAI)
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

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Brandt, EinarWigström, LarsWranne, Bengt

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