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  • 1.
    Albinsson, John
    et al.
    Lund Univ, Dept Biomed Engn, S-22100 Lund, Sweden..
    Brorsson, Sofia
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS).
    Rydén Ahlgren, Åsa
    Lund Univ, Dept Clin Sci, Clin Physiol & Nucl Med Unit, Malmo, Sweden..
    Cinthio, Magnus
    Lund Univ, Dept Biomed Engn, S-22100 Lund, Sweden..
    Improved tracking performance of lagrangian block-matching methodologies using block expansion in the time domain: In silico, phantom and invivo evaluations2014Ingår i: Ultrasound in Medicine and Biology, ISSN 0301-5629, E-ISSN 1879-291X, Vol. 40, nr 10, s. 2508-2520Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The aim of this study was to evaluate tracking performance when an extra reference block is added to a basic block-matching method, where the two reference blocks originate from two consecutive ultrasound frames. The use of an extra reference block was evaluated for two putative benefits: (i) an increase in tracking performance while maintaining the size of the reference blocks, evaluated using in silico and phantom cine loops; (ii) a reduction in the size of the reference blocks while maintaining the tracking performance, evaluated using in vivo cine loops of the common carotid artery where the longitudinal movement of the wall was estimated. The results indicated that tracking accuracy improved (mean - 48%, p<0.005 [in silico]; mean - 43%, p<0.01 [phantom]), and there was a reduction in size of the reference blocks while maintaining tracking performance (mean - 19%, p<0.01 [in vivo]). This novel method will facilitate further exploration of the longitudinal movement of the arterial wall. (C) 2014 World Federation for Ultrasound in Medicine & Biology.

  • 2.
    Pedersen, Eja
    et al.
    Högskolan i Halmstad, Akademin för ekonomi, teknik och naturvetenskap, Rydberglaboratoriet för tillämpad naturvetenskap (RLAS).
    Persson Waye, K.
    Department of Acoustics, Aalborg University, Aalborg, Denmark.
    Audio-visual reactions to wind turbines2003Ingår i: Acta Acoustica united with Acustica, ISSN 1610-1928, E-ISSN 1861-9959, Vol. 89, nr Suppl.Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A cross sectional study with the aim of evaluating dose response relationships between noise exposure from wind turbines and subjective reactions was performed in a flat landscape in Sweden. The study area comprised 16 turbines. Subjective reactions were obtained by a questionnaire, which purpose was masked. The response rate was 69% (356 respondents). Outdoor noise levels (dBA) were calculated for each dwelling at 2.5 dB intervals. The results showed a significant dose response relationship between noise level and annoyance. The prevalence of noise annoyance was comparatively high. In the categories of greatest noise exposure 37.5-40 dBA and >40 dBA, 20% (95%Cl: ±12.4) and 36% (95%Cl: ±18.4) were very annoyed. The noise only explained part of the annoyance. Noise annoyance was also correlated to visual factors such as the respondents’ opinion of the turbines’ impact on the landscape. To further study interactions between noise annoyance and visual disturbance, the shadows from wind turbines (hours/year) were calculated for each respondent and used as dose for annoyance of shadows, but also as a variable when trying to explain noise annoyance. The results of the analysis and the possible interactions between audio and visual annoyance will be presented at the conference.

  • 3.
    Polymeri, E.
    et al.
    Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Sadik, M.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Kaboteh, R.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Borrelli, P.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Enqvist, O.
    Department of Electrical Engineering, Region Västra Götaland, Chalmers University of Technology, Gothenburg, Sweden.
    Ulén, J.
    Eigenvision AB, Malmö, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Trägårdh, E.
    Department of Translational Medicine, Institute of Clinical Sciences, Lund University, Malmö, Sweden.
    Poulsen, M. H.
    Department of Urology, Odense University Hospital, Odense, Denmark.
    Simonsen, J. A.
    Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.
    Hoilund-Carlsen, P. F.
    Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.
    Johnsson, ÅA.
    Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Edenbrandt, L.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival2020Ingår i: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 40, nr 2, s. 106-113Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine

  • 4.
    Verikas, Antanas
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Gelzinis, Adas
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Bacauskiene, Marija
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Valincius, Donata
    Department of Applied Electronics, Kaunas University of Technology, Lithuania.
    Uloza, Virgilijus
    Kaunas University of Medicine, Lithuania.
    A kernel-based approach to categorizing laryngeal images2007Ingår i: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 31, nr 8, s. 587-594Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper is concerned with an approach to automated analysis of vocal fold images aiming to categorize laryngeal diseases. Colour, texture, and geometrical features are used to extract relevant information. A committee of support vector machines is then employed for performing the categorization of vocal fold images into healthy, diffuse, and nodular classes. The discrimination power of both, the original and the space obtained based on the kernel principal component analysis is investigated. A correct classification rate of over 92% was obtained when testing the system on 785 vocal fold images. Bearing in mind the high similarity of the decision classes, the correct classification rate obtained is rather encouraging.

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