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Automated feature detection in multidimensional images
Linköping University, Department of Biomedical Engineering. Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-29497Local ID: 14852ISBN: 91-85297-10-0 (print)OAI: oai:DiVA.org:liu-29497DiVA: diva2:250312
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
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. Kinematics of the heart: strain-rate imaging from time-resolved three-dimensional phase contrast MRI
Open this publication in new window or tab >>Kinematics of the heart: strain-rate imaging from time-resolved three-dimensional phase contrast MRI
Show others...
2002 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 21, no 9, 1105-1109 p.Article in journal (Refereed) Published
Abstract [en]

A four-dimensional mapping (three spatial dimensions + time) of myocardial strain-rate would help to describe the mechanical properties of the myocardium, which affect important physiological factors such as the pumping performance of the ventricles. Strain-rate represents the local instantaneous deformation of the myocardium and can be calculated from the spatial gradients of the velocity field. Strain-rate has previously been calculated using one-dimensional (ultrasound) or two-dimensional (2-D) magnetic resonance imaging techniques. However, this assumes that myocardial motion only occurs in one direction or in one plane, respectively. This paper presents a method for calculation of the time-resolved three-dimensional (3-D) strain-rate tensor using velocity vector information in a 3-D spatial grid during the whole cardiac cycle. The strain-rate tensor provides full information of both magnitude and direction of the instantaneous deformation of the myocardium. A method for visualization of the full 3-D tensor is also suggested. The tensors are visualized using ellipsoids, which display the principal directions of strain-rate and the ratio between strain-rate magnitude in each direction. The presented method reveals the principal strain-rate directions without a priori knowledge of myocardial motion directions.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-26711 (URN)10.1109/TMI.2002.804431 (DOI)11305 (Local ID)11305 (Archive number)11305 (OAI)
Available from: 2009-10-08 Created: 2009-10-08 Last updated: 2017-12-13
4. Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imaging
Open this publication in new window or tab >>Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imaging
Show others...
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We propose a robust approach for multimodality segmentation of the cardiac left ventricle. The method is based on the concept of deformable models, but extended with an enhanced and fast edge detection scheme that includes temporal information, and anatomical a priori information. The algorithm is implemented with a fast numeric scheme for solving energy minimization, and efficient filter nets for fast edge detection. This allows clinically applicable time for a whole time resolved 3D cardiac data set to be acheived on a standard desktop computer. The algorithm is validated on images acquired using MRI Gradient echo, MRl (SSFP) images, and Cardiac CT, and tested for feasibility with three other imaging modalities, including gated blood pool SPECT, echocardiography and late enhancement MRL.

Keyword
Image Segmentation, Deformable models, Left Ventricle, 3D, time resolved, edge detection
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-86175 (URN)
Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2016-03-14
5. Flow quantification from time resolved three dimensional phase contrast MRI
Open this publication in new window or tab >>Flow quantification from time resolved three dimensional phase contrast MRI
(English)Manuscript (preprint) (Other academic)
Abstract [en]

This paper presents novel techniques for deriving meaningful quantitative parameters from three dimensional velocity measurements. By shifting perspective from the Eulerian grid of measured velocities to a Lagrange perspective that follows flow using particle traces, we are able to divide flow into different groups based on their behavior. Parameters from each group can then be extracted and quantified. The applicability of the method is demonstrated by extracting parameters of diastolic flow in the human left ventricle.

Keyword
Flow quantification, velocity measurements, particle trace, left ventricle
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:liu:diva-86176 (URN)
Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2016-03-14

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Heiberg, Einar

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