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Time resolved three-dimensional segmentation of the left ventricle in multimodality cardiac imaging
Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Medicine and Care, Center for Medical Image Science and Visualization. Linköping University, Faculty of Health Sciences.
Department of Clinical Physiology, Lund University, Sweden.
Department of Medicine/Cardiology, University of California, San Francisco, USA.
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(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 [en]
Image Segmentation, Deformable models, Left Ventricle, 3D, time resolved, edge detection
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-86175OAI: oai:DiVA.org:liu-86175DiVA: diva2:575355
Available from: 2012-12-10 Created: 2012-12-10 Last updated: 2016-03-14
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

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Heiberg, EinarWigström, LarsKarlsson, Matts

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