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- Lundström, Jens, 1981-, et al.
(författare)
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Halmstad intelligent home - Capabilities and opportunities
- 2016
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Ingår i: Internet of Things Technologies for HealthCare. - Berlin : Springer Berlin/Heidelberg. - 9783319512334 ; , s. 9-15
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Konferensbidrag (refereegranskat)abstract
- Research on intelligent environments, such as smart homes, concerns the mechanisms that intelligently orchestrate the pervasive technical infrastructure in the environment. However, significant challenges are to build, configure, use and maintain these systems. Providing personalized services while preserving the privacy of the occupants is also difficult. As an approach to facilitate research in this area, this paper presents the Halmstad Intelligent Home and a novel approach for multioccupancy detection utilizing the presented environment. This paper also presents initial results and ongoing work. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016.
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| 2. |
- Morais, Pedro, et al.
(författare)
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Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging
- 2017
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Ingår i: Physics in Medicine and Biology. - : IOP PUBLISHING LTD. - 0031-9155 .- 1361-6560. ; 62:17, s. 6899-6919
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Tidskriftsartikel (refereegranskat)abstract
- Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 +/- 1.21 mm and 2.27 +/- 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.
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