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  • 251. Augustine, Robin
    Complex dielectric permittivity measurements of human skin and biological solution in2-67GHz range2012Conference paper (Refereed)
  • 252.
    Augustine, Robin
    Uppsala University.
    COmplex Fracture Orthopedic RehabiliTation – COMFORT2016Conference paper (Refereed)
  • 253.
    Augustine, Robin
    Uppsala University.
    Experimental procedure for determination of the dielectric properties of biological samples in the 2-50 GHz range2014In: IEEE Journal of Translational Engineering in Health and Medicine, E-ISSN 2168-2372Article in journal (Refereed)
  • 254. Augustine, Robin
    Human skin permittivity models for the millimeter-wave range2011In: IET Electronics Letters, Vol. 47, p. 427-428Article in journal (Refereed)
    Abstract [en]

    The complex permittivity of the human skin has been measured in vivo in the 10 –60 GHz range using a recently developed coaxial slim probe. The results are compared with the literature data at millimetre waves, and a broad-band Cole-Cole model is proposed for several locations on the arm, namely at the palm, wrist, and forearm. This reported study provides relevant data required for studying interactions between emerging body-centric wireless millimetre-wave technologies and the human body

  • 255.
    Augustine, Robin
    Uppsala University.
    Microwave antenna for analysis of mineralization in cranial vaults2015Conference paper (Other academic)
  • 256.
    Augustine, Robin
    Uppsala University.
    Microwave head phantoms for post-craniotomy and BMP based implant2015Conference paper (Other academic)
  • 257.
    Augustine, Robin
    Uppsala University.
    Microwave studies on Beta Tricalcium Phosphate Bioceramics for medical application2006Conference paper (Refereed)
  • 258.
    Augustine, Robin
    Uppsala University.
    Monitoring weight bearing in an ambulant setting: the SensiStep2016Conference paper (Refereed)
  • 259. Augustine, Robin
    Near-field dosimetry for the millimeter-wave exposure of human cells in vitro2012In: Bioelectromagnetics, ISSN 0197-8462, E-ISSN 1521-186X, p. 55-64Article in journal (Refereed)
    Abstract [en]

    Due to the expected mass deployment of millimeter-wave wireless technologies, thresholds of potential millimeter-wave-induced biological and health effects should be carefully assessed. The main purpose of this study is to propose, optimize, and characterize a near-field exposure configuration allowing illumination of cells in vitro at 60 GHz with power densities up to several tens of mW/cm(2) . Positioning of a tissue culture plate containing cells has been optimized in the near-field of a standard horn antenna operating at 60 GHz. The optimal position corresponds to the maximal mean-to-peak specific absorption rate (SAR) ratio over the cell monolayer, allowing the achievement of power densities up to 50 mW/cm(2) at least. Three complementary parameters have been determined and analyzed for the exposed cells, namely the power density, SAR, and temperature dynamics. The incident power density and SAR have been computed using the finite-difference time-domain (FDTD) method. The temperature dynamics at different locations inside the culture medium are measured and analyzed for various power densities. Local SAR, determined based on the initial rate of temperature rise, is in a good agreement with the computed SAR (maximal difference of 5%). For the optimized exposure setup configuration, 73% of cells are located within the ±3 dB region with respect to the average SAR. It is shown that under the considered exposure conditions, the maximal power density, local SAR, and temperature increments equal 57 mW/cm(2) , 1.4 kW/kg, and 6 °C, respectively, for the radiated power of 425 mW.

  • 260.
    Augustine, Robin
    Uppsala University.
    Phantom models for human hip and thigh2016Conference paper (Refereed)
  • 261. Augustine, Robin
    Polymeric ferrite sheets for SAR reduction of wearable antennas2010In: IET Electronics letters, Vol. 46, no 3, p. 197-199Article in journal (Refereed)
    Abstract [en]

    Reduction of specific absorption rate (SAR) has now become a buzz word because of the growing health concerns over microwave exposure. Ferrites are found to be effective in diminishing electromagnetic influence. In this reported work, flexible polymeric ferrite sheets are characterised on the basis of their shielding efficiencies. SAR measurements are carried out with a planar wearable antenna and polymeric ferrite shielding to confirm its competence.

  • 262. Augustine, Robin
    Polymeric ferrite-loaded antennas for on-body communications2009In: Microwave and Optical technology Letters, Vol. 51, no 11Article in journal (Refereed)
    Abstract [en]

    Wearable antennas are integral part of body area networks (BANs). Antenna design for BAN applications is a challenging task since the antennas have to be small, efficient, and must not be affected by the wearer's body. This makes isolation of antenna a matter of importance. Ferrites form an opaque media for microwave at the 2.4-GHz ISM band and hence it could be used for the isolation of antenna from surroundings. Thin polymeric ferrite sheets are used to reduce body influence in BAN perspective.

  • 263.
    Augustine, Robin
    Uppsala University.
    SRR Antenna for Biomedical Application2016Conference paper (Refereed)
  • 264.
    Augustine, Robin
    Uppsala University.
    Transverse Electromagnetic cell for Biological cell exposure studies Sujith2016Conference paper (Refereed)
  • 265.
    Augustine, Robin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Kurup, Dhanesh G.
    Amrita Vishva Vidyapeetham Univ, Dept Elect & Commun, Bangalore, Karnataka, India.
    Raman, Sujith
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Lee, Dujin
    Gwangju Inst Sci & Technol, Dept Med Syst Engn, Gwangju, South Korea.
    Kim, K. Kangwook
    Gwangju Inst Sci & Technol, Sch Informat & Mechatron, Gwangju, South Korea.
    Rydberg, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    Bone Mineral Density Analysis using Ultra Wideband Microwave Measurements2015Conference paper (Refereed)
    Abstract [en]

    A novel approach to analyze the bone mineral density (BMD) based on microwave reflectivity analysis is presented in this paper. The proposed method enables us to overcome the health risks associated with diagnostic techniques such as X-rays for repeated study of the rate of mineralization in the case of fractures or de-mineralization in the case of osteoporosis. In this paper, we demonstrate the application of Microwaves for continuous observation of skull healing process during post-cranial surgery period. The proposed technique can be a potential clinical model in future for extracting target characteristics such as bone deposition thickness and other cranial defects. Based on the conclusions of wideband measured data, we propose to design the Transceiver using ultra wideband (UWB) pulsed technology.

  • 266. Ausen, Dag
    et al.
    Westvik, Rita
    Svagård, Ingrid
    Österlund, Lars
    Gustafson, Inga
    Vikholm-Lundin, Inger
    Winquist, Fredrik
    Lading, Lars
    Gran, Jens
    Foresight Biomedical Sensors2007Report (Other academic)
    Abstract [en]

    The foresight study on biomedical sensors has addressed different approaches with future use of biomedical sensors in the health care sector, like: How will biomedical sensors shape the healthcare systems of the future? How can they impact the quality and cost of healthcare and what are the business opportunities in the Nordic region?

  • 267.
    Avenel, Christophe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Blur detection and visualization in histological whole slide images2015In: Proc. 10th International Conference on Mass Data Analysis of Images and Signals, Leipzig, Germany: IBaI , 2015Conference paper (Refereed)
    Abstract [en]

    Digital pathology holds the promise of improved workflow and also of the use of image analysis to extract features from tissue samples for quantitative analysis to improve current subjective analysis of, for example, cancer tissue. But this requires fast and reliable image digitization. In this paper we address image blurriness, which is a particular problem with very large images or tissue micro arrays scanned with whole slide scanners, since autofocus methods may fail when there is a large variation in image content. We introduce a method to detect, quantify and dis-play blurriness from whole slide images (WSI) in real-time. We describe a blurriness measurement based on an ideal high pass filter in the frequency domain. In contrast with other method our method does not require any prior knowledge of the image content, and it produces a continuous blurriness map over the entire WSI. This map can be displayed as an overlay of the original data and viewed at different levels of magnification with zoom and pan features. The computation time for an entire WSI is around 5 minutes on an average workstation, which is about 180 times faster than existing methods.

  • 268.
    Avenel, Christophe
    et al.
    CADESS Med AB, Uppsala, Sweden.
    Tolf, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Dragomir, Anca
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Clinical and experimental pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. CADESS Med AB, Uppsala, Sweden.
    Glandular Segmentation of Prostate Cancer: An Illustration of How the Choice of Histopathological Stain Is One Key to Success for Computational Pathology2019In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 7, article id 125Article in journal (Refereed)
    Abstract [en]

    Digital pathology offers the potential for computer-aided diagnosis, significantly reducing the pathologists' workload and paving the way for accurate prognostication with reduced inter-and intra-observer variations. But successful computer-based analysis requires careful tissue preparation and image acquisition to keep color and intensity variations to a minimum. While the human eye may recognize prostate glands with significant color and intensity variations, a computer algorithm may fail under such conditions. Since malignancy grading of prostate tissue according to Gleason or to the International Society of Urological Pathology (ISUP) grading system is based on architectural growth patterns of prostatic carcinoma, automatic methods must rely on accurate identification of the prostate glands. But due to poor color differentiation between stroma and epithelium from the common stain hematoxylin-eosin, no method is yet able to segment all types of glands, making automatic prognostication hard to attain. We address the effect of tissue preparation on glandular segmentation with an alternative stain, Picrosirius red-hematoxylin, which clearly delineates the stromal boundaries, and couple this stain with a color decomposition that removes intensity variation. In this paper we propose a segmentation algorithm that uses image analysis techniques based on mathematical morphology and that can successfully determine the glandular boundaries. Accurate determination of the stromal and glandular morphology enables the identification of the architectural pattern that determine the malignancy grade and classify each gland into its appropriate Gleason grade or ISUP Grade Group. Segmentation of prostate tissue with the new stain and decomposition method has been successfully tested on more than 11000 objects including well-formed glands (Gleason grade 3), cribriform and fine caliber glands (grade 4), and single cells (grade 5) glands.

  • 269.
    Axelson, Mattias
    Linköping University, Department of Biomedical Engineering.
    A Physiological investigation of Rest in Commercial Long-Haul Truck Drivers2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 270.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Imlook4d: introducing an extendable research 4d analysis software2014In: XII Turku PET Symposium, 24-27 May 2014, Turku, Finland: the symposium of Nordic Association for Clinical Physics (NACP), 2014, p. 63-63Conference paper (Other academic)
    Abstract [en]

    Imlook4d (http://www.dicom-port.com) is a free Matlab based graphical user interface (GUI) tool useful for static, dynamic and gated PET studies.  It supports reading and writing DICOM, Nifti, Analyze, ECAT.  The DICOM reader is orders of magnitude faster than the Matlab imaging toolbox.  Imlook4d requires no additional Matlab toolboxes.

    The main benefit with imlook4d is that it is easily extendable with scripts, accessing exported variables such as the image matrix (4D) and a region-of-interest (ROI) matrix.  Scripts are available via a menu in the imlook4d GUI, and can be used to manipulate the image-matrix and ROI data.  There is also a menu option to export and import these variables to the Matlab workspace for interactive manipulation, useful for one-off fixes or for script development.  There are presently about 30 scripts in categories such as ROI, Matrix, Header info etc.  There is also direct export to ImageJ [1] and import back from ImageJ, thus giving access to all tools available within ImageJ.

    Imlook4d has a built in volume-of-interest editor, with a brush tool for quick interactive ROI delineation, and via scripts, different ways of thresholding ROIs from parts of the image.  Time activity data is saved to a tab-delimited text file.

    The principal-component (PC) based Hotelling filter is an integrated part of the program, which allows for interactive noise reduction without loss of quantitation [2].  A typical work flow for a dynamic data set is to turn on the filter for ROI delineation, and then there is the choice of turning it off for export of time-activity data.  Also the PC images can be used to draw ROIs on, which under some circumstances gives enhanced contrast.

    Calculation of parametric pharmacokinetic modelling images can be performed interactively, calculated slice by slice as the user scrolls through the volume.  Reference models for Patlak, Logan and Averaged Simple Flow Model [3]  applied on 15O-water are implemented, and it is relatively easy to implement other kinetic models.  Similarly, scripts have been developed for regional Patlak and Logan models on ROI data.

    [1] Rasband, WS, ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, http://imagej.nih.gov/ij/, 1997-2014

    [2] Axelsson J, Sörensen J, The 2D Hotelling filter - a quantitative noise-reducing principal-component filter for dynamic PET data, with applications in patient dose reduction. BMC Med Phys. 2013 Apr 10;13:1. doi: 10.1186/1756-6649-13-1.

    [3] Yoshida, K, Mullani, N and Gould KL, Coronary Flow and Flow Reserve by PET Simplified for Clinical Applications Using Rubidium-82 or Nitrogen-13-Ammonia, J Nucl Med 1996; 37:1701-1712

    Figure 1.  The imlook4d GUI with the user SCRIPTS menu selected.  The group of ROI scripts was further selected.  In the underlying image, a rough ROI is created.  

  • 271.
    Axelsson, Jan
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sörensen, Jens
    PET-center, Department of Radiology, Oncology and Radiation Sciences, Uppsala University, Uppsala, Sweden.
    The 2D Hotelling filter: a quantitativenoise-reducing principal-component filter fordynamic PET data, with applications in patientdose reduction2013In: BMC Medical Physics, ISSN 1756-6649, Vol. 13, no 1Article in journal (Refereed)
    Abstract [en]

    Background: In this paper we apply the principal-component analysis filter (Hotelling filter) to reduce noise fromdynamic positron-emission tomography (PET) patient data, for a number of different radio-tracer molecules. Wefurthermore show how preprocessing images with this filter improves parametric images created from suchdynamic sequence.We use zero-mean unit variance normalization, prior to performing a Hotelling filter on the slices of a dynamictime-series. The Scree-plot technique was used to determine which principal components to be rejected in thefilter process. This filter was applied to [11C]-acetate on heart and head-neck tumors, [18F]-FDG on liver tumors andbrain, and [11C]-Raclopride on brain. Simulations of blood and tissue regions with noise properties matched to realPET data, was used to analyze how quantitation and resolution is affected by the Hotelling filter. Summing varyingparts of a 90-frame [18F]-FDG brain scan, we created 9-frame dynamic scans with image statistics comparable to 20MBq, 60 MBq and 200 MBq injected activity. Hotelling filter performed on slices (2D) and on volumes (3D) werecompared.Results: The 2D Hotelling filter reduces noise in the tissue uptake drastically, so that it becomes simple to manuallypick out regions-of-interest from noisy data. 2D Hotelling filter introduces less bias than 3D Hotelling filter in focalRaclopride uptake. Simulations show that the Hotelling filter is sensitive to typical blood peak in PET prior to tissueuptake have commenced, introducing a negative bias in early tissue uptake. Quantitation on real dynamic data isreliable. Two examples clearly show that pre-filtering the dynamic sequence with the Hotelling filter prior toPatlak-slope calculations gives clearly improved parametric image quality. We also show that a dramatic dosereduction can be achieved for Patlak slope images without changing image quality or quantitation.Conclusions: The 2D Hotelling-filtering of dynamic PET data is a computer-efficient method that gives visuallyimproved differentiation of different tissues, which we have observed improve manual or automated regionof-interest delineation of dynamic data. Parametric Patlak images on Hotelling-filtered data display improved clarity,compared to non-filtered Patlak slope images without measurable loss of quantitation, and allow a dramaticdecrease in patient injected dose.

  • 272.
    Axelsson, Pelle
    et al.
    KTH, School of Technology and Health (STH).
    Torelm, Marcus
    KTH, School of Technology and Health (STH).
    Cost-benefit-analys av optisk 3D-scanner2011Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
  • 273. Ayllnon, David
    et al.
    Gil-Pita, Roberto
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical Engineering, Medical sensors, signals and systems.
    Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 6, article id e0156522Article in journal (Refereed)
    Abstract [en]

    Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

  • 274.
    Ayllon, David
    et al.
    Universidad de Alcalá.
    Gil-Pita, Roberto
    Universidad de Alcalá.
    Seoane, Fernando
    University of Borås, Faculty of Textiles, Engineering and Business. KTH-School of Technology and Health.
    Detection and Classification of Measurement Errors in Bioimpedance Spectroscopy2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 6, article id e0156522Article in journal (Refereed)
    Abstract [en]

    Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible.

  • 275.
    Ayllon, David
    et al.
    Department of Signal Theory and Communications.
    Seoane, Fernando
    KTH, School of Technology and Health (STH), Medical sensors, signals and systems (MSSS).
    Gil-Pita, Roberto
    Department of Signal Theory and Communications.
    Cole equation and parameter estimation from electrical bioimpedance spectroscopy measurements: A comparative study2009In: EMBC: 2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20, Buenos Aires: IEEE Engineering in Medicine and Biology , 2009, p. 3779-3782Conference paper (Refereed)
    Abstract [en]

    Since there are several applications of Electrical Bioimpedance (EBI) that use the Cole parameters as base of the analysis, to fit EBI measured data onto the Cole equation is a very common practice within Multifrequency-EBI and spectroscopy. The aim of this paper is to compare different fitting methods for EBI data in order to evaluate their suitability to fit the Cole equation and estimate the Cole parameters. Three of the studied fittings are based on the use of Non-Linear Least Squares on the Cole model, one using the real part only, a second using the imaginary part and the third using the complex impedance. Furthermore, a novel fitting method done on the impedance plane, without using any frequency information has been implemented and included in the comparison. Results show that the four methods perform relatively well but the best fitting in terms of standard error of estimate is the fitting obtained from the resistance only. The results support the possibility of measuring only the resistive part of the bioimpedance to accurately fit Cole equation and estimate the Cole parameters, with entailed advantages.

  • 276. Azar, J.C.
    et al.
    Hamid Muhammed, Hamed
    KTH, School of Technology and Health (STH), Medical Engineering.
    Automated Tracking of the Carotid Artery in Ultrasound Image Sequences Using a Self Organizing Neural Network2010In: Proceedings of 20th International Conference on Pattern Recognition (ICPR 2010), Istanbul, Turkey, Istanbul, Turkey, 2010, p. 2548-2551Conference paper (Refereed)
    Abstract [en]

    An automated method for the segmentation and tracking of moving vessel walls in 2D ultrasound image sequences is introduced. The method was tested on simulated and real ultrasound image sequences of the carotid artery. Tracking was achieved via a self organizing neural network known as Growing Neural Gas. This topology-preserving algorithm assigns a net of nodes connected by edges that distributes itself within the vessel walls and adapts to changes in topology with time. The movement of the nodes was analyzed to uncover the dynamics of the vessel wall. By this way, radial and longitudinal strain and strain rates have been estimated. Finally, wave intensity signals were computed from these measurements. The method proposed improves upon wave intensity wall analysis, WIWA, and opens up a possibility for easy and efficient analysis and diagnosis of vascular disease through noninvasive ultrasonic examination.

  • 277.
    Azar, Jimmy
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Automated Tissue Image Analysis Using Pattern Recognition2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Automated tissue image analysis aims to develop algorithms for a variety of histological applications. This has important implications in the diagnostic grading of cancer such as in breast and prostate tissue, as well as in the quantification of prognostic and predictive biomarkers that may help assess the risk of recurrence and the responsiveness of tumors to endocrine therapy.

    In this thesis, we use pattern recognition and image analysis techniques to solve several problems relating to histopathology and immunohistochemistry applications. In particular, we present a new method for the detection and localization of tissue microarray cores in an automated manner and compare it against conventional approaches.

    We also present an unsupervised method for color decomposition based on modeling the image formation process while taking into account acquisition noise. The method is unsupervised and is able to overcome the limitation of specifying absorption spectra for the stains that require separation. This is done by estimating reference colors through fitting a Gaussian mixture model trained using expectation-maximization.

    Another important factor in histopathology is the choice of stain, though it often goes unnoticed. Stain color combinations determine the extent of overlap between chromaticity clusters in color space, and this intrinsic overlap sets a main limitation on the performance of classification methods, regardless of their nature or complexity. In this thesis, we present a framework for optimizing the selection of histological stains in a manner that is aligned with the final objective of automation, rather than visual analysis.

    Immunohistochemistry can facilitate the quantification of biomarkers such as estrogen, progesterone, and the human epidermal growth factor 2 receptors, in addition to Ki-67 proteins that are associated with cell growth and proliferation. As an application, we propose a method for the identification of paired antibodies based on correlating probability maps of immunostaining patterns across adjacent tissue sections.

    Finally, we present a new feature descriptor for characterizing glandular structure and tissue architecture, which form an important component of Gleason and tubule-based Elston grading. The method is based on defining shape-preserving, neighborhood annuli around lumen regions and gathering quantitative and spatial data concerning the various tissue-types.

  • 278.
    Azar, Jimmy
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Histological Stain Evaluation for Machine Learning Applications2012In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, 2012Conference paper (Refereed)
  • 279.
    Azar, Jimmy
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Microarray Core Detection by Geometric Restoration2012In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 35, no 5-6, p. 381-393Article in journal (Refereed)
    Abstract [en]

    Whole-slide imaging of tissue microarrays (TMAs) holds the promise of automated image analysis of a large number of histopathological samples from a single slide. This demands high-throughput image processing to enable analysis of these tissue samples for diagnosis of cancer and other conditions. In this paper, we present a completely automated method for the accurate detection and localization of tissue cores that is based on geometric restoration of the core shapes without placing any assumptions on grid geometry. The method relies on hierarchical clustering in conjunction with the Davies-Bouldin index for cluster validation in order to estimate the number of cores in the image wherefrom we estimate the core radius and refine this estimate using morphological granulometry. The final stage of the algorithm reconstructs circular discs from core sections such that these discs cover the entire region of each core regardless of the precise shape of the core. The results show that the proposed method is able to reconstruct core locations without any evidence of localization error. Furthermore, the algorithm is more efficient than existing methods based on the Hough transform for circle detection. The algorithm's simplicity, accuracy, and computational efficiency allow for automated high-throughput analysis of microarray images.

  • 280.
    Azar, Jimmy C.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Simonsson, Martin
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Image segmentation and identification of paired antibodies in breast tissue2014In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, p. 647273:1-11Article in journal (Refereed)
    Abstract [en]

    Comparing staining patterns of paired antibodies designed towards a specific protein but toward different epitopes of the protein provides quality control over the binding and the antibodies' ability to identify the target protein correctly and exclusively. We present a method for automated quantification of immunostaining patterns for antibodies in breast tissue using the Human Protein Atlas database. In such tissue, dark brown dye 3,3'-diaminobenzidine is used as an antibody-specific stain whereas the blue dye hematoxylin is used as a counterstain. The proposed method is based on clustering and relative scaling of features following principal component analysis. Our method is able (1) to accurately segment and identify staining patterns and quantify the amount of staining and (2) to detect paired antibodies by correlating the segmentation results among different cases. Moreover, the method is simple, operating in a low-dimensional feature space, and computationally efficient which makes it suitable for high-throughput processing of tissue microarrays.

  • 281.
    Azar, Jimmy
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Simonsson, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Hast, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated Classification of Glandular Tissue by Statistical Proximity Sampling2015In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, article id 943104Article in journal (Refereed)
    Abstract [en]

    Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.

  • 282. Azimi, I.
    et al.
    Anzanpour, A.
    Rahmani, A. M.
    Liljeberg, P.
    Tenhunen, Hannu
    KTH, School of Information and Communication Technology (ICT), Industrial and Medical Electronics.
    Self-aware early warning score system for IoT-based personalized healthcare2017In: International Summit on eHealth 360°, 2016, Springer, 2017, p. 49-55Conference paper (Refereed)
    Abstract [en]

    Early Warning Score (EWS) system is specified to detect and predict patient deterioration in hospitals. This is achievable via monitoring patient's vital signs continuously and is often manually done with paper and pen. However, because of the constraints in healthcare resources and the high hospital costs, the patient might not be hospitalized for the whole period of the treatments, which has lead to a demand for in-home or portable EWS systems. Such a personalized EWS system needs to monitor the patient at anytime and anywhere even when the patient is carrying out daily activities. In this paper, we propose a self-aware EWS system which is the reinforced version of the existing EWS systems by using the Internet of Things technologies and the self-awareness concept. Our self-aware approach provides (i) system adaptivity with respect to various situations and (ii) system personalization by paying attention to critical parameters. We evaluate the proposed EWS system using a full system demonstration. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017.

  • 283.
    Azzouzi, Sawsen
    et al.
    University of Sousse, Tunisia.
    Patra, Hirak Kumar
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Ben Ali, Mounir
    University of Sousse, Tunisia.
    Nooredeen Abbas, Mohammed
    National Research Centre, Egypt.
    Dridi, Cherif
    Centre Research Microelect and Nanotechnol CRMN Sousse, Tunisia.
    Errachid, Abdelhamid
    University of Lyon 1, France.
    Turner, Anthony
    Linköping University, Department of Physics, Chemistry and Biology, Biosensors and Bioelectronics. Linköping University, Faculty of Science & Engineering.
    Citrate-selective electrochemical mu-sensor for early stage detection of prostate cancer2016In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 228, p. 335-346Article in journal (Refereed)
    Abstract [en]

    The extremely specialised anatomical function of citrate inside the prostate, make it one of the preferred biomarkers for early stage detection of prostate cancer. However, current detection methods are seriously limited due to the very low citrate concentrations that need to be measured in order to follow disease progression. In the present work, we report a novel citrate-selective-sensor based on iron (III) phthalocyanine chloride-C-monoamido-Poly-n-Butyl Acrylate (Fe(III)MAPcC1 P n BA) modified gold -electrodes for the electrochemical determination and estimation of the pathophysiological range of citrate. The newly synthesised ionophore has been structurally characterised using Fourier transform infrared (FTIR) and UV-vis spectroscopy. Contact angle measurements and atomic force microscopy (AFM) have been used to investigate the adhesion and morphological properties of the membrane. The developed citrate-selective-electrodes had a Nernstian sensitivity of-19.34 +/- 0.83 mV/decade with a detection limit of about 9 x 10-6M and a linear range from 4 x 10(-5)M to 10(-1) M, which covered the pathologically important clinical range. Electrochemical impedance spectroscopy (EIS) showed very high sensitivity with a lower Limit of detection 1.7 x 10(-9) M and linear detection range (10(-8)-10(-1) M), which is very important not only for the early-stage diagnosis and screening procedures, but also in mapping the stage of the cancer too. (C) 2016 Elsevier B.V. All rights reserved.

  • 284.
    Baban, Hanna
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Grauning, Olivia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Using Fetal Myocardial Velocity Recordings to Evaluate an AI Platform to Predict High-risk Deliveries2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Diagnosing abnormal fetal cardiac function using ultrasound is a complicated procedure which makes it difficult to obtain high quality results from ultrasound examinations that are performed shortly before delivery. Color tissue Doppler imaging (cTDI) is the echocardiographic technique that has been used to obtain the data for this project. Subtle changes in the fetal cardiac function caused by a variety of complications can possibly be detected using cTDI. Fetuses suffering from these complications are often involved in high-risk deliveries. Combining the data obtained from cTDI with Artificial Intelligence (AI) may improve precision and accuracy when it comes to diagnosing pathological conditions involving fetal cardiac function before delivery. AI uses machines to perform and execute tasks that are characteristic of human intelligence. AI can be achieved by using deep learning. Deep learning uses algorithms called artificial neural networks that are inspired by the biological structure and function of the human brain. The neural networks classify information in a similar manner to the human brain. A platform that uses deep learning can make statements or predictions based on the data fed to it. The AI platform Peltarion uses deep learning to perform tasks. The aim of this project was to use Peltarion to evaluate the possibility of predicting high-risk deliveries with abnormal perinatal outcome by using data obtained by cTDI velocity recordings of the fetal heart. The data included myocardial velocity recordings from 107 pregnancies, out of the 107 pregnancies 82 of the babies were born healthy while 25 babies had an adverse perinatal outcome. The data was uploaded in the platform and three models were built and trained in order to evaluate the performance of the platform using the data. The parameters that have been used to determine the results are loss, accuracy and precision. The results showed that the accuracy parameter was measured to be 0.8 in all cases which means that the model correctly predicts if a fetal heart is healthy or likely to have an adverse outcome 80% of the time. The precision parameter was measured to be around 0.4 which means out of all the times the model predicted a fetal heart to have an adverse outcome, only 40% truly had an adverse outcome. It was concluded that a substantially larger amount of evenly distributed data is required to appropriately evaluate the possibility of using fetal myocardial velocity recordings as data for the AI platform Peltarion to predict high-risk deliveries.

  • 285.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Case Based Reasoningin Support of the LVAD Surgical Treatment2013In: Medicinteknikdagarna 2013, Electronic Proceedings, 2013Conference paper (Refereed)
  • 286.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Peterzen, Bengt
    Östergötlands Läns Landsting, Heart and Medicine Center.
    Lönn, Urban
    Östergötlands Läns Landsting, Heart and Medicine Center.
    Casimir Ahn, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Heart and Medicine Center, Department of Thoracic and Vascular Surgery.
    Case Based Reasoning in a Web Based Decision Support System for Thoracic Surgery2013In: IFMBE Proceedings 41 / [ed] L.M. Roa Romero, Springer, 2013, p. 1413-1416Conference paper (Refereed)
    Abstract [en]

    Case Based Reasoning (CBR) methodology provides means of collecting patients cases and retrieving them following the clinical criteria. By studying previously treated patients with similar backgrounds, the physician can get a better base for deciding on treatment for a current patient and be better prepared for complications that might occur during and after surgery. This could be taken advantage of when there is not enough data for a statistical analysis, but electronic patient records that provide all the relevant information to assure a timely and accurate clinical insight into a patient particular situation.

    We have developed and implemented a CBR engine using the Nearest Neighbor algorithm. A patient case is represented as a combination of perioperative variable values and operation reports. Physicians could review a selected number of cases by browsing through the electronic patient record and operational narratives which provides an exhaustive insight into the previously treated cases. An evaluation of the search algorithm suggests a very good functionality.

  • 287.
    Babic, Ankica
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Soerheim, Helen
    University of Bergen, Norway.
    M-Health ApplicationProduct Development for Physiological Disorders Based on Interaction Design2013In: Medicinteknikdagarna 2013, Electronic Proceedings, 2013Conference paper (Refereed)
  • 288.
    Backlin, Eric
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine.
    A comparator for spectroscopic work1930In: Review of Scientific Instruments, ISSN 0034-6748, E-ISSN 1089-7623, Vol. 1, no 11, p. 662-666Article in journal (Refereed)
  • 289. Badariah Asan, Noor
    et al.
    Hassan, Emadeldeen
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Department of Electronics and Electrical Communications, Menoufia University, Menouf, Egypt.
    Velander, Jacob
    Redzwan Mohd Shah, Syaiful
    Noreland, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Blokhuis, Taco J.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Berggren, Martin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Voigt, Thiemo
    Augustine, Robin
    Characterization of the Fat Channel for Intra-Body Communication at R-Band Frequencies2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 9, article id 2752Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7–2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of ∼0.7 dB and ∼1.9 dB per cm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.

  • 290.
    Bae, Sang Won
    et al.
    Kyonggi University, Suwon, South Korea.
    Korman, Matias
    Tohoku University, Sendai, Japan.
    Mitchell, Joseph SB
    Stony Brook University, New York, USA.
    Okamoto, Yoshio
    The University of Electro-Communications, Tokyo, Japan.
    Polishchuk, Valentin
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.
    Wang, Haitao
    Utah State University, Utah, USA.
    Computing the $ L_1 $ Geodesic Diameter and Center of a Polygonal Domain2016In: 33rd Symposium on Theoretical Aspects of Computer Science / [ed] Nicolas Ollinger; Heribert Vollmer, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik , 2016, Vol. 47, p. 14:1-14:14Conference paper (Refereed)
    Abstract [en]

    For a polygonal domain with h holes and a total of n vertices, we present algorithms that compute the L1 geodesic diameter in O(n2+h4) time and the L1 geodesic center in O((n4+n2h4) (n)) time, where (·) denotes the inverse Ackermann function. No algorithms were known for these problems before. For the Euclidean counterpart, the best algorithms compute the geodesic diameter in O(n7.73) or O(n7(h+log n)) time, and compute the geodesic center in O(n12+) time. Therefore, our algorithms are much faster than the algorithms for the Euclidean problems. Our algorithms are based on several interesting observations on L1 shortest paths in polygonal domains.

  • 291.
    Baeza Ortega, José Antonio
    Linköping University, Center for Medical Image Science and Visualization, CMIV. Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Combined Visualization of Intracardiac Blood Flow and Wall Motion Assessed by MRI2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    MRI is a well known and widely spread technique to characterize cardiac pathologies due to its high spatial resolution, its accessibility and its adjustable contrast among soft tissues.

    In attempt to close the gap between blood flow, myocardial movement and the cardiac fucntion, researching in the MRI field addresses the quantification of some of the most relevant blood and myocardial parameters.

    During this proyect a new tool that allows reading, postprocessing, quantifying and visualizing 2D motion sense MR data has been developed. In order to analyze intracardiac blood flow and wall motion, the new tool quantifies velocity, turbulent kinetic energy, pressure and strain.

    In the results section the final tool is presented, describing the visualization modes, which represent the quantified parameters both individually and combined; and detailing auxiliary tool features as masking, thresholding, zooming, and cursors.

    Finally, thecnical aspects as the convenience of two dimensional examinations to create compact tools, and the challenges of masking as part of the relative pressure calculation, among others, are discussed; ending up with the proposal of some future developments.

  • 292.
    Bagge, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Farmakognosi.
    Chromatography of Therapeutic Peptides - Contrasting SFC and HPLC2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This work is a comparison of a well-established and a novel, "green" and efficient technique to separate peptides of pharmaceutical interest. An attempt is made to derive the chromatographic retention behaviour from these techniques to a number of property descriptors derived from the linear sequence of amino acids. A set of therapeutic peptides were carefully chosen to be experimentally evaluated using in silico-based descriptor calculations. A principle component analysis was performed to assess the distribution of calculated descriptors for including peptides with variable properties. A diluent optimization study was also included to find the optimal diluent for peptides with minimal diluent effects and peak splitting phenomena. The results showed that the solvents tert-butanol and methanol performed best between 20-30 and 50 volumetric percent water as additive in SFC and HPLC, respectively. These diluents were then used for the peptides within the set to evaluate the retention and selectivity in HPLC and SFC. SFC performed well in terms of resolving power. Inparticular, SFC was able to separate Leuprolide and Triptorelin while HPLC was not. A comparison was also made in between the two stationary phases CN and XT, where a global selectivity was shown to be higher for CN.

    This work does also assess a novel method for determining solubility of analytes in supercritical fluid. The method was evaluated using the pharmaceutical compounds caffeine and aspirin and then used to determine solubility of Leu-Enkephalin in 20% (v/v%) methanol. The solubility of caffeine was determined to be 0.45 mg ml-1 in pure SF-CO2 under 140 bar pressure and 3.9 mg ml-1 for aspirin in 2.4% methanol. Both values correlated well with measurements from four acknowledged papers within this field. Leu-Enkephalin was found to have a solubility of 1.90 mg ml-1 using a solvent corresponding to the initial phase condition of the gradient used for peptide analysis in SFC. Further experimental work is required before the method can be implemented as a useful tool in preparative chromatography, however the results presented here show the compatibility of assessing biomolecules in both pure SF-CO2 and mixed with modifier. The possibility to determine solubility with additional modifier infers an important step of including and evaluating these compounds creating a solid support to subsequent large scale separation.

  • 293.
    Bagheri, Maryam
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Rezakhani, Arjang
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Roghani, Mehrdad
    Neurophysiology Research Center, Shahed University, Iran.
    Joghataei, Mohammad T.
    Iran University of Medical Science, Iran.
    Mohseni, Simin
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes2015In: Journal of Visualized Experiments, ISSN 1940-087X, E-ISSN 1940-087X, no 106, p. e53113-Article in journal (Refereed)
    Abstract [en]

    As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify their functional and structural properties, a condition called reactive astrogliosis. Here, a protocol for assessment of the morphological properties of astrocytes is presented. This protocol includes quantification of 12 different parameters i.e. the surface area and volume of the tissue covered by an astrocyte (astrocyte territory), the entire astrocyte including branches, cell body, and nucleus, as well as total length and number of branches, the intensity of fluorescence immunoreactivity of antibodies used for astrocyte detection, and astrocyte density (number/1,000 mu m(2)). For this purpose three-dimensional (3D) confocal microscopic images were created, and 3D image analysis software such as Volocity 6.3 was used for measurements. Rat brain tissue exposed to amyloid beta(1-40) (A beta(1-40)) with or without a therapeutic intervention was used to present the method. This protocol can also be used for 3D morphometric analysis of other cells from either in vivo or in vitro conditions.

  • 294.
    Bai, Guohua
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    An Organic View of Prototyping in Information System Development2014In: 2014 IEEE 17th International Conference on Computational Science and Engineering (CSE) / [ed] Liu, X; ElBaz, D; Hsu, CH; Kang, K; Chen, W, ChengDu: IEEE, 2014, Vol. Article number 07023844, p. 1814-1818Conference paper (Refereed)
    Abstract [en]

    This paper presents an organic view of prototyping for managing dynamic factors involved in evolutionary design of information systems (IS). Those dynamic factors can be caused by, for example, continuing suggestions from users, changes in the technologies, and users-designers learning related stepwise progresses. Expanding the evolutionary prototyping to ‘start small and grow’, the organic view of prototyping proposes two prerequisites to do so, namely 1) a sustainable and adaptive ‘embryo’ – an organic structure of the future system, and 2) an embedded learning and feedback management that the actors of the system (users, designers, decision makers, administrators) can communicate with each other. An example of eHealth system design demonstrates how the prerequisites can be implemented.

  • 295.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Gholamhosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Connolly, M. J.
    University of Auckland, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Advanced decision support system for older adults2015In: Studies in Health Technology and Informatics, vol. 211, 2015, p. 235-240Conference paper (Refereed)
    Abstract [en]

    Decision support systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia and to generate early warnings. 

  • 296.
    Baig, M. M.
    et al.
    Auckland University of Technology, New Zealand.
    GholamHosseini, H.
    Auckland University of Technology, New Zealand.
    Moqeem, A. A.
    Auckland University of Technology, New Zealand.
    Mirza, F.
    Auckland University of Technology, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption2017In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, no 7, article id 115Article in journal (Refereed)
    Abstract [en]

    The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology. 

  • 297.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Hosseini, H. G.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine learning-based clinical decision support system for early diagnosis from real-time physiological data2016In: Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc. , 2016, p. 2943-2946, article id 7848584Conference paper (Refereed)
    Abstract [en]

    This research aims to design a self-organizing decision support system for early diagnosis of key physiological events. The proposed system consists of pre-processing, clustering and diagnostic system, based on self-organizing fuzzy logic modeling. The clustering technique was employed with empirical pattern analysis, particularly when the information available is incomplete or the data model is affected by vagueness, which is mostly the case with medical/clinical data. Clustering module can be viewed as unsupervised learning from a given dataset. This module partitions the patient vital signs to identify the key relationships, patterns and clusters among the medical data. Secondly, it uses self-organizing fuzzy logic modeling for early symptom and event detection. Based on the clustering outcome, when detecting abnormal signs, a high level of agreement was observed between system interpretation and human expert diagnosis of the physiological events and signs. © 2016 IEEE.

  • 298.
    Baig, M.M.
    et al.
    Auckland University of Technology.
    GholamHosseini, H.
    Auckland University of Technology.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care2015In: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2015, p. 1215-1218Conference paper (Refereed)
    Abstract [en]

    Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet computers which are increasingly integrating into the healthcare mainstream as clinical decision support systems. Despite the benefits of table-based healthcare applications, there are concerns around the accuracy, security and stability of such applications. In this study, we developed five tablet-based application screens for remote patient monitoring at hospital care settings and identified related issues and challenges. The ultimate aim of this research is to integrate decision support algorithms into the monitoring system in order to improve inpatient care and the effectiveness of such applications.

  • 299. Bajic, Buda
    et al.
    Suveer, Amit
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gupta, Anindya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Pepic, Ivana
    Lindblad, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sladoje, Natasa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement2018In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper (Refereed)
  • 300. Bajić, Buda
    et al.
    Lindblad, Joakim
    Sladoje, Nataša
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    An evaluation of potential functions for regularized image deblurring2014In: Image Analysis and Recognition: Part I, Springer Berlin/Heidelberg, 2014, p. 150-158Conference paper (Refereed)
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