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  • 51.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lönn, Urban
    Linköping Heart Center Linköping University.
    Peterzén, Bengt
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Anaesthesiology. Östergötlands Läns Landsting, Anaesthesiology and Surgical Centre, Department of Intensive Care UHL.
    Granfeldt, Hans
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Ahn, Henrik Casimir
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Thoracic Surgery. Östergötlands Läns Landsting, Heart Centre, Department of Thoracic and Vascular Surgery.
    Hemopump treatment in patients with postcardiotomy heart failure1995In: Annals of Thoracic Surgery, ISSN 0003-4975, E-ISSN 1552-6259, Vol. 60, p. 1067-1071Article in journal (Refereed)
  • 52.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Maojo, Victor
    University of Madrid Spain.
    Martin-Sanchez, Fernando
    Inst of Health Carlos I Madrid Spain.
    Santos, Miguel
    University of Aveiro Portugal.
    Sousa, Antonio
    University of Aveiro Portugal.
    The INFOGENMED project: A Biomedical informatics approach to integrate heterogeneous biological and clinical information2005In: ERCIM news, ISSN 1564-0094, Vol. 60, no JanuaryArticle in journal (Refereed)
  • 53.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Mathiesen, Ulrik
    Oskarshamn County Hospital Sweden.
    Hedin, Kristina
    Linköping University, Department of Molecular and Clinical Medicine.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Assessing an AI knowledge-Base for asymptomatic liver diseases1998In: AMIA98,1998, Philadelphia: Hanley & Belfuse , 1998, p. 513-Conference paper (Refereed)
  • 54.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Olivier, José Luis
    University of Aveiro, Portugal.
    Voznuka, Natalja
    Linköpings universitet.
    Oliviera, Ilidio
    University of Aveiro, Portugal.
    Storm, Markus
    Linköpings universitet.
    Maojo, Victor
    Universidad Politecnica de Madrid, Spain.
    Sanchez, Fernando
    Instituto de Salud Carlos III, Spain.
    Santos, Miguel
    Genomica STAB VIDA, Portugal.
    Pereira, Antonio Sousa
    University of Aveiro, Portugal.
    Confidentiality and security issues in web services managing patient clinical and genetic data2004Report (Other academic)
  • 55.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Petelin, Milan
    University of Ljubljana .
    Ivanusa, Teodora
    University of Ljubljana .
    Convergen assessment of radiographic diagnostic systems1997In: IEEE Symposium on Computer-Based Medical Systemss,1997, Washington: IEEE Computer , 1997, p. 205-Conference paper (Refereed)
  • 56.
    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.

  • 57.
    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)
  • 58.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Ster, Branko
    Computer and Inforamtion Science University of Ljubljana.
    Pavesic, Nikola
    Electrical Engineering University of Ljubljana.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Machine Learning for the quality of life in inflammatory bowel disease1997In: Medical Informatics Europe97,1997, Amsterdam: IOS Press , 1997, p. 661-Conference paper (Refereed)
  • 59.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Zganec, Mario
    University of Ljubljana .
    Palcic, Branko
    Cancer Research Centre BC Canada.
    3D presentation of the nuclear cell features in quantitative cytometry1996In: AMIA 1996,1996, Washington: Hanley & Belfus , 1996, p. 679-Conference paper (Refereed)
  • 60.
    Babic, Ankica
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Åhlfeldt, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Wigertz, Ove
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Bodemar, Göran
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Molecular and Clinical Medicine, Gastroenterology and Hepatology. Östergötlands Läns Landsting, Centre for Medicine, Department of Endocrinology and Gastroenterology UHL.
    Mathiesen, Ulrik
    Oskarshamn Hospital .
    Artificial neural networks in clustering and classification of data on unspecified liver diseases1993In: Nordic Meeting on Medical and Biomeidical engineering,1993, 1993, p. 136-Conference paper (Refereed)
  • 61.
    Balkanyi, Laszlo
    et al.
    European Centre for Disease Prevention and Control, Stockholm, Sweden.
    Schulz, Stefan
    Medizinische Universität Graz, Austria and Freiburg University Medical Center, Freiburg, Germany.
    Cornet, Ronald
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Bodenreider, Olivier
    National Library of Medicine, Bethsheda, USA.
    Medical concept representation: the years beyond 2000.2013In: Proceedings of Studies in Health Technology & Informatics, vol. 192, IOS Press, 2013, Vol. 192, p. 1011-1011Conference paper (Refereed)
    Abstract [en]

    This work aims at understanding the state of the art in the broad contextual research area of "medical concept representation". Our data support the general understanding that the focus of research has moved toward medical ontologies, which we interpret as a paradigm shift. Both the opinion of socially active groups of researchers and changes in bibliometric data since 1988 support this opinion. Socially active researchers mention the OBO foundry, SNOMED CT, and the UMLS as anchor activities.

  • 62.
    Barlow, Lotti
    et al.
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Gerdin, Ulla
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Almborg, Ann-Helene
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Kron, Bengt
    Regionkansliet Hälso- och sjukvårdsavdelningen, Västra Götalandsregionen.
    Lindberg, Christina
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Bränd Persson, Kristina
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Ahlzén, Karin
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Ericsson, Erika
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Adelöf, Anna
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Karlsson, Daniel
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Wolff Foster, Lisa
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Widigson, Lena
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Bratt, Maria
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Testi, Stefano
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Staerner Steen, Anna
    Fackspråk och informatik, Regler och tillstånd, Socialstyrelsen.
    Nyström, Mikael
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Nationellt fackspråk för vård och omsorg: Slutrapport2011Report (Other (popular science, discussion, etc.))
    Abstract [sv]

    Sammanfattning

    Ett tillgängligt och använt nationellt fackspråk ska bidra till en god och säker vård och omsorg. Det ska även medverka till att kvaliteten och resultaten på området ska kunna följas upp och jämföras på ett mer effektivt sätt. Slutrapporten presenterar resultatet av projektet Nationellt fackspråk för vård och omsorg samt förslag till förvaltning och utveckling.

    Resultatet

    Resultatet innefattar bland annat att det internationella begreppssystemet Snomed CT är översatt till svenska och att det är förberett för förvaltning och distribution. Socialstyrelsen har även tagit fram och testat metoder för förvaltning och utveckling av det nationella fackspråket i sin helhet. Därtill har representanter för målgrupperna informerats och fått kunskap.

    Rapporten innehåller en utförlig beskrivning av det nationella fackspråkets sammantagna innehåll: Socialstyrelsens termbank, klassifikationer och kodverk, den svenska versionen av Snomed CT, metoder för utveckling och förvaltning samt regler för användning.

    Förvaltning, införande och resursbehov

    I rapporten finns förslag till hur hela det nationella fackspråket kan tas omhand av Socialstyrelsen och hur det kan införas i vården och omsorgen. Projektets övergång till en långsiktigt hållbar organisation kräver resurser. Därför redogör rapporten för det förväntade resursbehovet för förvaltning och utveckling. Bland annat föreslås en treårig utbildningsinsats samt stimulansbidrag för införande.

    Krav på styrning, samordning och förtydligat ansvar

    Rapporten betonar behovet av en samlad och medveten styrning av utvecklingen inom området. Socialstyrelsen vill ha en samordnande roll i utvecklingen och förvaltningen av det nationella fackspråket. Myndigheten föreslås få det initiala ansvaret för att utbilda användare och att driva frågor om det nationella fackspråket.

    Vidare vill Socialstyrelsen få ett uttalat mandat att samordna de nationella aktiviteter som drivs med koppling till Snomed CT. Rapporten pekar ut några särskilt prioriterade områden som myndigheten borde få i uppdrag att arbeta vidare inom.

    Kunskapsstyrning och normgivning

    En viktig slutsats i rapporten är att användningen av det nationella fackspråket behöver regleras för att målet om ökad säkerhet för klienter och patienter ska kunna uppnås. I dagsläget bedöms föreskrifter vara den metod som bäst kan garantera ett brett genomförande.

    Målgrupper för slutrapporten

    Slutrapporten riktar sig till beslutsfattare i kommuner och landsting, vård- och omsorgspersonal med särskilt intresse eller ansvar för dokumentationsfrågor och professionella organisationer. Den riktar sig också till terminologiansvariga i kommuner och landsting, IT-direktörer, IT-leverantörer samt aktörer inom den nationella strategin för eHälsa.

  • 63.
    Baud, Robert
    et al.
    Medical Informatics Division University Hospital of Geneva.
    Nyström, Mikael
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borin, Lars
    Inst för svenska språket Göteborgs universitet.
    Ewans, Roger
    The Information Technology Research Institute University of Brighton.
    Schulz, Stefan
    Inst of Medical Biometry and Medical Informatics Albert-Ludwigs-Universität, Freiburg.
    Zweigenbaum, Pierre
    STIM/DSI Assistance Publique-Hopitaux de Paris.
    Interchanging Lexical information for a multilingual dictionary2005In: AMIA Annual Symposium Proceedings, Washington DC: AMIA Symposium; American Medical Informatics Association , 2005, p. 31-35Conference paper (Refereed)
    Abstract [en]

    OBJECTIVE:

    To facilitate the interchange of lexical information for multiple languages in the medical domain. To pave the way for the emergence of a generally available truly multilingual electronic dictionary in the medical domain.

    METHODS:

    An interchange format has to be neutral relative to the target languages. It has to be consistent with current needs of lexicon authors, present and future. An active interaction between six potential authors aimed to determine a common denominator striking the right balance between richness of content and ease of use for lexicon providers.

    RESULTS:

    A simple list of relevant attributes has been established and published. The format has the potential for collecting relevant parts of a future multilingual dictionary. An XML version is available.

    CONCLUSION:

    This effort makes feasible the exchange of lexical information between research groups. Interchange files are made available in a public repository. This procedure opens the door to a true multilingual dictionary, in the awareness that the exchange of lexical information is (only) a necessary first step, before structuring the corresponding entries in different languages.

  • 64.
    Behjat, Hamid
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Statistical Parametric Mapping of fMRI data using Spectral Graph Wavelets2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In typical statistical parametric mapping (SPM) of fMRI data, the functional data are pre-smoothed using a Gaussian kernel to reduce noise at the cost of losing spatial specificity. Wavelet approaches have been incorporated in such analysis by enabling an efficient representation of the underlying brain activity through spatial transformation of the original, un-smoothed data; a successful framework is the wavelet-based statistical parametric mapping (WSPM) which enables integrated wavelet processing and spatial statistical testing. However, in using the conventional wavelets, the functional data are considered to lie on a regular Euclidean space, which is far from reality, since the underlying signal lies within the complex, non rectangular domain of the cerebral cortex. Thus, using wavelets that function on more complex domains such as a graph holds promise. The aim of the current project has been to integrate a recently developed spectral graph wavelet transform as an advanced transformation for fMRI brain data into the WSPM framework. We introduce the design of suitable weighted and un-weighted graphs which are defined based on the convoluted structure of the cerebral cortex. An optimal design of spatially localized spectral graph wavelet frames suitable for the designed large scale graphs is introduced. We have evaluated the proposed graph approach for fMRI analysis on both simulated as well as real data. The results show a superior performance in detecting fine structured, spatially localized activation maps compared to the use of conventional wavelets, as well as normal SPM. The approach is implemented in an SPM compatible manner, and is included as an extension to the WSPM toolbox for SPM.

  • 65.
    Berg Andersen, Per
    et al.
    University of Bergen, Norway .
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway .
    Self-reporting for Bipolar Patients through Smartphone2014In: IFMBE Proceedings / [ed] Laura M. Roa Romero, Springer, 2014, Vol. 41, p. 1358-1361Conference paper (Refereed)
    Abstract [en]

    Self-reporting of symptoms is widely used and validated in the field of psychiatry, also in the context of bipolar disorder. This paper presents work on a self-reporting system for bipolar patients using a smartphone to gather data from the patient, which is communicated to a server via a secure connection. The data is presented in a web application to a patient for his/hers self-monitoring, and to medical personnel associated with the treatment of the patient. The work described here is part of an ongoing system development and gives insights into the field research and motivation for choosing Life Charting Methodology as a structural element. Leaning on such well accepted and validated therapeutic tools should secure validity and feasibility of the final system that would appear to patients as familiar and easy to use. Consequently, the application is expected to be directly understandable to everyone involved in the treatment. Programming solutions will capture the essence, but will be adjusted to the electronic environment which will be validated for its correctness and user-friendliness.

  • 66.
    Bergnéhr, Leo
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Segmentation and Alignment of 3-D Transaxial Myocardial Perfusion Images and Automatic Dopamin Transporter Quantification2008Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Nuclear medical imaging such as SPECT (Single Photon Emission Tomography) is an imaging modality which is readily used in many applications for measuring physiological properties of the human body. One very common type of examination using SPECT is when measuring myocardial perfusion (blood flow in the heart tissue), which is often used to examine e.g. a possible myocardial infarction (heart attack). In order for doctors to give a qualitative diagnose based on these images, the images must first be segmented and rotated by a medical technologist. This is performed due to the fact that the heart of different patients, or for patients at different times of examination, is not situated and rotated equally, which is an essential assumption for the doctor when examining the images. Consequently, as different technologists with different amount of experience and expertise will rotate images differently, variability between operators arises and can often become a problem in the process of diagnosing.

    Another type of nuclear medical examination is when quantifying dopamine transporters in the basal ganglia in the brain. This is commonly done for patients showing symptoms of Parkinson’s disease or similar diseases. In order to specify the severity of the disease, a scheme for calculating different fractions between parts of the dopamine transporter area is often used. This is tedious work for the person performing the quantification, and despite the acquired three dimensional images, quantification is too often performed on one or more slices of the image volume. In resemblance with myocardial perfusion examinations, variability between different operators can also here present a possible source of errors.

    In this thesis, a novel method for automatically segmenting the left ventricle of the heart in SPECT-images is presented. The segmentation is based on an intensity-invariant local-phase based approach, thus removing the difficulty of the commonly varying intensity in myocardial perfusion images. Additionally, the method is used to estimate the angle of the left ventricle of the heart. Furthermore, the method is slightly adjusted, and a new approach on automatically quantifying dopamine transporters in the basal ganglia using the DaTSCAN radiotracer is proposed.

  • 67.
    Bergquist, Urban
    et al.
    Inst för medicinsk teknik Linköpings universitet.
    Babic, Ankica
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Aspects of certainty in patient classification using a Health-related Quality-of-Life instrument in inflammatory bowel disease1999In: AMIA99,1999, Philadelphia: Hanley & Belfus Inc , 1999, p. 202-Conference paper (Refereed)
  • 68.
    Berntsen, Eirik
    et al.
    University of Bergen, Norway.
    Babic, Ankica
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. University of Bergen, Norway.
    Cherry: mobile application for children with cancer2013In: MEDINFO 2013: proceedings of the 14th World Congress on Medical and Health Informatics / [ed] Christoph Ulrich Lehmann, Elske Ammenwerth, Christian Nøhr, IOS Press, 2013, p. 1168-Conference paper (Other academic)
    Abstract [en]

    The Cherry project seeks to address the information needs of young cancer patients, their parents, and health care providers. It aims at helping the patients to understand various aspects of their disease and treatment, and allow them to assess and record their disease related quality of life. It uses elements of social media to offer a meeting point with the physician and peers. Information is presented in a way that is both understandable and appealing to young children in school age and adolescents. Preschool children will be studied as a separate user group to address their needs and possibilities to meet them. The Cherry system wants to utilize Internet and mobile technologies to benefit patient outcome.

  • 69. Björnemo, M.
    et al.
    Brun, Anders
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Kikinis, R.
    Westin, C.-F.
    Regularized stochastic white matter tractography using diffusion tensor MRI2002In: Medical Image Computing and Computer-Assisted Intervention MICCAI02,2002, 2002Conference paper (Refereed)
  • 70.
    Bolger, Ann F
    et al.
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Heiberg, Einar
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Dyverfeldt, Petter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Carlsson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Johansson, P
    Markenroth, K
    Sigfridsson, Andreas
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Engvall, Jan
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Ebbers, Tino
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Health Sciences, Clinical Physiology . Östergötlands Läns Landsting, Heart Centre, Department of Clinical Physiology.
    Arheden, H
    Tredimensionellt MR-blodflöde och diastolisk kinetisk energi kvantiferat med magnetisk resonanstomografi efter kirurgisk vänsterkammarrekonstruktion. Ny teknik för utvärdering av kammarfunktion.2007In: Riksstämman,2007, 2007Conference paper (Other academic)
  • 71.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization, CMIV.
    Center for medical image science and visualization (CMIV) a unique cross-disciplinary environment for medical image processing research2005In: Nordic Baltic Conference on Medical Engineering and Medical Physics,2005, 2005, p. 192-Conference paper (Refereed)
  • 72.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Andersson, Mats
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Generation of representations for supervised learning - A velocity estimation example2001In: SCIA 2001,2001, 2001Conference paper (Refereed)
  • 73.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Thord
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images2016In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, p. 3146-3149Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for atlas-based segmentation of medical images. The method uses semi- supervised learning of a graph describing a manifold of anatom- ical variations of whole-body images, where unlabelled data are used to find a path with small deformations from the labelled atlas to the target image. The method is evaluated on 36 whole-body magnetic resonance images with manually segmented livers as ground truth. Significant improvement (p < 0.001) was obtained compared to direct atlas-based registration. 

  • 74.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Dahlqvist, Leinhard Olof
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Improvement in Magnetic Resonance Imaging Relating to Correction of Chemical Shift Artifact and Intensity Inhomogeneity2011Patent (Other (popular science, discussion, etc.))
    Abstract [en]

    Present invention discloses systems and methods for improvement of magnetic resonance images. Correction of a chemical shift artefact in an image acquired from a magnetic resonance imaging system is obtained by a system and a method involving iterative - compensation for the misregistration effect in an image domain. Correction of an intensity inhomogeneity in such images is obtained by a system and a method involving locating voxels corresponding to pure adipose tissue and estimating correction field from these points.

  • 75.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A canonical correlation approach to exploratory data analysis in fMRI2002Conference paper (Other academic)
    Abstract [en]

    A computationally efficient data-driven method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The proposed method is more robust and much more computationally efficient than independent component analysis, which previously has been applied in fMRI.

  • 76.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Friman, Ola
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Blind Source Separation of Functional MRI Data2002Conference paper (Other academic)
  • 77.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A canonical correlation approach to blind source separation2001Report (Other academic)
  • 78.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    An Adaptive Stereo Algorithm Based on Canonial Correlation Analysis1998Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel algorithm that uses CCA and phase analysis to detect the disparity in stereo images. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. The algorithm can even handle cases where the images have different scales. The algorithm can also handle depth discontinuities and give multiple depth estimates for semitransparent images.

  • 79.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Canonical correlation analysis in early version processing2001In: European Symposium on Artificial neural Networks ESANN,2001, 2001Conference paper (Refereed)
  • 80.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Estimating Multiple Depths in Semi-transparent Stereo Images1999In: Proceedings of the 11th Scandinavian Conference on Image Analysis, 1999Conference paper (Refereed)
    Abstract [en]

    A stereo algorithm that can estimate multiple depths in semi-transparent images is presented. The algorithm is based on a combination of phase analysis and canonical correlation analysis. The algorithm adapts filters in each local neighbourhood of the image in a way which maximizes the correlation between the filtered images. The adapted filters are then analysed to find the disparity. This is done by a simple phase analysis of the scalar product of the filters. For images with different but constant depths, a simple reconstruction procedure is suggested.

  • 81.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Finding Efficient Nonlinear Visual Operators using Canonical Correlation Analysis2000In: Proceedings of the SSAB Symposium on Image Analysis: Halmstad, Linköping: Linköpings universitet , 2000, p. 13-16Conference paper (Refereed)
    Abstract [en]

    This paper presents a general strategy for designing efficient visual operators. The approach is highly task oriented and what constitutes the relevant information is defined by a set of examples. The examples are pairs of images displaying a strong dependence in the chosen feature but are otherwise independent. Particularly important concepts in the work are mutual information and canonical correlation. Visual operators learned from examples are presented, e.g. local shift invariant orientation operators and image content invariant disparity operators. Interesting similarities to biological vision functions are observed.

  • 82.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Landelius, Tomas
    n/a.
    Learning Canonical Correlations1997In: SCIA10: Lappeenranta, Finland, 1997Conference paper (Refereed)
  • 83.
    Borga, Magnus
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Malmgren, Helge
    Dept of Philosophy Göteborgs universitet.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Feature selective edge detektion2000In: International Conference on Pattern Recognition,2000, IEEE , 2000, p. 229-232 vol.1Conference paper (Refereed)
    Abstract [en]

    We present a method that finds edges between certain image features, e.g. gray-levels, and disregards edges between other features. The method uses a channel representation of the features and performs normalized convolution using the channel values as certainties. This means that areas with certain features can be disregarded by the edge filter. The method provides an important tool for finding tissue specific edges in medical images, as demonstrated by an MR-image example

  • 84.
    Borga, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Rydell, Joakim
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Signal and Anatomical Constraints in Adaptive Filtering of fMRI Data2007In: Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007: From Nano to Macro, IEEE , 2007, p. 432-435Conference paper (Refereed)
    Abstract [en]

    An adaptive filtering method for fMRI data is presented. The method is related to bilateral filtering, but with a range filter that takes into account local similarities in signal as well as in anatomy. Performance is demonstrated on simulated and real data. It is shown that using both these similarity constraints give better performance than if only one of them is used, and clearly better than standard low-pass filtering.

  • 85.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Thomas, E. Louise
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Romu, Thobias
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Rosander, Johannes
    Advanced MR Analytics AB, Linköping, Sweden.
    Fitzpatrick, Julie
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Bell, Jimmy D
    Department of Life Sciences Faculty of Science and Technology University of Westminster, London, United Kingdom.
    Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies2015In: NMR in Biomedicine, ISSN 1099-1492, Vol. 28, no 12, p. 1747-1753Article in journal (Refereed)
    Abstract [en]

    Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as magnetic resonance imaging (MRI) has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles for the use of MRI in large scale studies. In this study we assess the validity of the recently proposed fat-muscle-quantitation-system (AMRATM Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images.  Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using SliceOmatic, the current gold-standard, and the AMRATM Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by both analysis methods, (Pearson correlation r = 0.97 p<0.001), with the AMRATM Profiler analysis being significantly faster (~3 mins) than the conventional SliceOmatic approach (~40 mins). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 vs SliceOmatic 4.73 ± 1.75 litres, p=0.97). For the AMRATM Profiler analysis, the intra-observer coefficient of variation was 1.6 % for IAAT and 1.1 % for ASAT, the inter-observer coefficient of variation was 1.4 % for IAAT and 1.2 % for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRATM Profiler, opening up the possibility of large-scale human phenotypic studies.

  • 86.
    Borga, Magnus
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Virtanen, Kirsi A.
    Turku PET Centre, University of Turku, Finland.
    Romu, Thobias
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Dahlqvist Leinhard, Olof
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics.
    Persson, Anders
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.
    Nuutila, Pirjo
    Turku PET Centre, University of Turku, Finland.
    Enerbäck, Sven
    Department of Biomedicine, University of Gothenburg, Sweden.
    Brown adipose tissue in humans: detection and functional analysis using PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography)2014In: Methods in Enzymology: Methods of Adipose Tissue Biology / [ed] Ormond MacDougald, Elsevier, 2014, 1, p. 141-159Chapter in book (Other academic)
    Abstract [en]

    Research with the aim to translate findings of the beneficial effects induced by brown adipose tissue (BAT) on metabolism, as seen in various non-human experimental systems to also include human metabolism requires tools that accurately measure how BAT influences human metabolism. This review sets out to discuss such techniques, how they can be used, what they can measure and also some of their limitations. The focus is on detection and functional analysis of human BAT and how this can be facilitated by applying advanced imaging technology such as:  PET (Positron Emission Tomography), MRI (Magnetic Resonance Imaging), and DECT (Dual Energy Computed Tomography).

  • 87.
    Breitenmoser, Sabina
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Evaluation and implementation of neural brain activity detection methods for fMRI2005Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique used to study brain functionality to enhance our understanding of the brain. This technique is based on MRI, a painless, noninvasive image acquisition method without harmful radiation. Small local blood oxygenation changes which are reflected as small intensity changes in the MR images are utilized to locate the active brain areas. Radio frequency pulses and a strong static magnetic field are used to measure the correlation between the physical changes in the brain and the mental functioning during the performance of cognitive tasks.

    This master thesis presents approaches for the analysis of fMRI data. The constrained Canonical Correlation Analysis (CCA) which is able to exploit the spatio-temporal nature of an active area is presented and tested on real human fMRI data. The actual distribution of active brain voxels is not known in the case of real human data. To evaluate the performance of the diagnostic algorithms applied to real human data, a modified Receiver Operating Characteristics (modified ROC) which deals with this lack of knowledge is presented. The tests on real human data reveal the better detection efficiency with the constrained CCA algorithm.

    A second aim of this thesis was to implement the promising technique of constrained CCA into the software environment SPM. To implement the constrained CCA algorithms into the fMRI part of SPM2, a toolbox containing Matlab functions has been programmed for the further use by neurological scientists. The new SPM functionalities to exploit the spatial extent of the active regions with CCA are presented and tested.

  • 88.
    Brodin, Henrik
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    CCASENSE: Canonical Correlation Analysis for Estimation of Sensitivity Maps for Fast MRI2006Independent thesis Basic level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Magnetic Resonance Imaging is an established technology for both imaging and functional studies in clinical and research environments. The field is still very research intense. Two major research areas are acquisition time and signal quality. The last decade has provided tools for more efficient possibilities of trading these factors against each other through parallel imaging. In this thesis one parallel imaging method, Sensitivity Encoding for fast MRI (SENSE) is examined. An alternative solution CCASENSE is developed. CCASENSE reduces the acquisition time by estimating the sensitivity maps required for SENSE to work instead of running a reference scan. The estimation process is done by Blind Source Separation through Canonical Correlation Analysis. It is shown that CCASENSE appears to estimate the sensitivity maps better than ICASENSE which is a similar algorithm.

  • 89.
    Brun, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Manifold learning and representations for image analysis and visualization2006Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    We present a novel method for manifold learning, i.e. identification of the low-dimensional manifold-like structure present in a set of data points in a possibly high-dimensional space. The main idea is derived from the concept of Riemannian normal coordinates. This coordinate system is in a way a generalization of Cartesian coordinates in Euclidean space. We translate this idea to a cloud of data points in order to perform dimension reduction. Our implementation currently uses Dijkstra's algorithm for shortest paths in graphs and some basic concepts from differential geometry. We expect this approach to open up new possibilities for analysis of e.g. shape in medical imaging and signal processing of manifold-valued signals, where the coordinate system is “learned” from experimental high-dimensional data rather than defined analytically using e.g. models based on Lie-groups.

    We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps (Belkin and Niyogi, 2002), we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.

  • 90.
    Brun, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Manifolds in Image Science and Visualization2007Doctoral thesis, monograph (Other academic)
    Abstract [en]

    A Riemannian manifold is a mathematical concept that generalizes curved surfaces to higher dimensions, giving a precise meaning to concepts like angle, length, area, volume and curvature. A glimpse of the consequences of a non-flat geometry is given on the sphere, where the shortest path between two points – a geodesic – is along a great circle. Different from Euclidean space, the angle sum of geodesic triangles on the sphere is always larger than 180 degrees.

    Signals and data found in applied research are sometimes naturally described by such curved spaces. This dissertation presents basic research and tools for the analysis, processing and visualization of such manifold-valued data, with a particular emphasis on future applications in medical imaging and visualization.

    Two-dimensional manifolds, i.e. surfaces, enter naturally into the geometric modelling of anatomical entities, such as the human brain cortex and the colon. In advanced algorithms for processing of images obtained from computed tomography (CT) and ultrasound imaging (US), images themselves and derived local structure tensor fields may be interpreted as two- or three-dimensional manifolds. In diffusion tensor magnetic resonance imaging (DT-MRI), the natural description of diffusion in the human body is a second-order tensor field, which can be related to the metric of a manifold. A final example is the analysis of shape variations of anatomical entities, e.g. the lateral ventricles in the brain, within a population by describing the set of all possible shapes as a manifold.

    Work presented in this dissertation include: Probabilistic interpretation of intrinsic and extrinsic means in manifolds. A Bayesian approach to filtering of vector data, removing noise from sampled manifolds and signals. Principles for the storage of tensor field data and learning a natural metric for empirical data.

    The main contribution is a novel class of algorithms called LogMaps, for the numerical estimation of logp (x) from empirical data sampled from a low-dimensional manifold or geometric model embedded in Euclidean space. The logp (x) function has been used extensively in the literature for processing data in manifolds, including applications in medical imaging such as shape analysis. However, previous approaches have been limited to manifolds where closed form expressions of logp (x) have been known. The introduction of the LogMap framework allows for a generalization of the previous methods. The application of LogMaps to texture mapping, tensor field visualization, medial locus estimation and exploratory data analysis is also presented.

  • 91.
    Brun, Anders
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Björnemo, M.
    Kikinis, R.
    Westin, C.-F.
    White matter tractography using sequental importance sampling2002In: ISMRM 02,2002, 2002Conference paper (Refereed)
  • 92.
    Brun, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Tensor Glyph Warping - Visualizing Metric Tensor Fields using Riemannian Exponential Maps2009In: Visualization and Processing of Tensor Fields: Advances and Perspectives / [ed] Laidlaw, David H.; Weickert, Joachim, Springer Berlin/Heidelberg, 2009, p. 139-160Chapter in book (Refereed)
    Abstract [en]

    The Riemannian exponential map, and its inverse the Riemannian logarithm map, can be used to visualize metric tensor fields. In this chapter we first derive the well-known metric sphere glyph from the geodesic equations, where the tensor field to be visualized is regarded as the metric of a manifold. These glyphs capture the appearance of the tensors relative to the coordinate system of the human observer. We then introduce two new concepts for metric tensor field visualization: geodesic spheres and geodesically warped glyphs. These additions make it possible not only to visualize tensor anisotropy, but also the curvature and change in tensorshape in a local neighborhood. The framework is based on the exp maps, which can be computed by solving a second order Ordinary Differential Equation (ODE) or by manipulating the geodesic distance function. The latter can be found by solving the eikonal equation, a non-linear Partial Differential Equation (PDE), or it can be derived analytically for some manifolds. To avoid heavy calculations, we also include first and second order Taylor approximations to exp and log. In our experiments, these are shown to be sufficiently accurate to produce glyphs that visually characterize anisotropy, curvature and shape-derivatives in smooth tensor fields. 

  • 93.
    Brun, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Park, Hae-Jeong
    Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA, USA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
    Shenton, Martha E.
    Clinical Neuroscience Division, Laboratory of Neuroscience, Boston VA, USA Health Care System-Brockton Division, Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
    Westin, Carl-Fredrik
    Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA.
    Clustering Fiber Traces Using Normalized Cuts2004In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004: 7th International Conference, Saint-Malo, France, September 26-29, 2004. Proceedings, Part I, Springer Berlin/Heidelberg, 2004, p. 368-375Conference paper (Refereed)
    Abstract [en]

    In this paper we present a framework for unsupervised segmentation of white matter fiber traces obtained from diffusion weighted MRI data. Fiber traces are compared pairwise to create a weighted undirected graph which is partitioned into coherent sets using the normalized cut (Ncut) criterion. A simple and yet effective method for pairwise comparison of fiber traces is presented which in combination with the Ncut criterion is shown to produce plausible segmentations of both synthetic and real fiber trace data. Segmentations are visualized as colored stream-tubes or transformed to a segmentation of voxel space, revealing structures in a way that looks promising for future explorative studies of diffusion weighted MRI data.

  • 94.
    Brun, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Centre for Image Analysis, SLU, Uppsala, Sweden.
    Martin-Fernandez, Marcos
    Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
    Acar, Burac
    Boğaziçi University 5 Electrical & Electronics Engineering Department Istanbul Turkey.
    Munoz-Moreno, Emma
    Universidad de Valladolid Laboratorio de Procesado de Imagen (LPI), Dept. Teoría de la Señal y Comunicaciones e Ingeniería Telemática Spain.
    Cammoun, Leila
    Signal Processing Institute (ITS), Ecole Polytechnique Fédérale Lausanne (EPFL) Lausanne Switzerland.
    Sigfridsson, Andreas
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV). Center for Technology in Medicine, Dept. Señales y Comunicaciones, University of Las Palmas de Gran Canaria, Spain.
    Sosa-Cabrera, Dario
    Center for Technology in Medicine, Dept. Señales y Comunicaciones, University of Las Palmas de Gran Canaria, Spain.
    Svensson, Björn
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Herberthson, Magnus
    Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Similar Tensor Arrays - A Framework for Storage of Tensor Array Data2009In: Tensors in Image Processing and Computer Vision / [ed] Santiago Aja-Fern´andez, Rodrigo de Luis Garc´ıa, Dacheng Tao, Xuelong Li, Springer Science+Business Media B.V., 2009, 1, p. 407-428Chapter in book (Refereed)
    Abstract [en]

    This chapter describes a framework for storage of tensor array data, useful to describe regularly sampled tensor fields. The main component of the framework, called Similar Tensor Array Core (STAC), is the result of a collaboration between research groups within the SIMILAR network of excellence. It aims to capture the essence of regularly sampled tensor fields using a minimal set of attributes and can therefore be used as a “greatest common divisor” and interface between tensor array processing algorithms. This is potentially useful in applied fields like medical image analysis, in particular in Diffusion Tensor MRI, where misinterpretation of tensor array data is a common source of errors. By promoting a strictly geometric perspective on tensor arrays, with a close resemblance to the terminology used in differential geometry, (STAC) removes ambiguities and guides the user to define all necessary information. In contrast to existing tensor array file formats, it is minimalistic and based on an intrinsic and geometric interpretation of the array itself, without references to other coordinate systems.

  • 95.
    Brun, Anders
    et al.
    Centre for Image Analysis, Swedish University of Agricultural Sciences, Sweden.
    Nilsson, Ola
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Reimers, Martin
    Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Norway.
    Museth, Ken
    Linköping University, Department of Science and Technology, Digital Media. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Computing Riemannian Normal Coordinates on Triangle MeshesManuscript (preprint) (Other academic)
    Abstract [en]

    Imagine an ant walking around on the curved surface of a plant, a radio amateur planning to broadcast to a distant location across the globe or a pilot taking o from an airport - all of them are helped by egocentric maps of the world around them that shows directions and distances to various remote places. It is not surprising that this idea has already been used in cartography, where it is known as Azimuthal Equidistant Projection (AEP). If Earth is approximated by a sphere, distances and directions between two places are computed from arcs along great circles. In physics and mathematics, the same idea is known as Riemannian Normal Coordinates (RNC). It has been given a precise and general denition for surfaces (2-D), curved spaces (3-D) and generalized to smooth manifolds (N-D). RNC are the Cartesian coordinates of vectors that index points on the surface (or manifold) through the so called exponential map, which is a well known concept in dierential geometry. They are easily computed for a particular point if the inverse of the exponential map, the logarithm map, is known. Recently, RNC and similar coordinate systems have been used in computer graphics, visualization and related areas of research. In Fig. 1 for instance, RNC are used to produce a texture on the Stanford bunny through decal compositing. Given the growing use of RNC, which is further elaborated on in the next section, it is meaningful to develop accurate and reproducible techniques to compute this parameterization. In this paper, we describe a technique to compute RNC for surfaces represented by triangular meshes, which is the predominant representation of surfaces in computer graphics. The method that we propose has similarities to the Logmap framework, which has previously been developed for dimension reduction of unorganized point clouds in high-dimensional spaces, a.k.a. manifold learning. For this reason we sometimes refer to it as "Logmap for triangular meshes" or simply Logmap.

  • 96.
    Brun, Anders
    et al.
    Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA .
    Park, Hae-Jeong
    Clinical Neuroscience Div., Lab. of Neuroscience, Boston VA Health Care System-Brockton Division, Dep. of Psychiatry, Harvard Medical School, and Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, .
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA .
    Coloring of DT-MRI fiber traces using Laplacian Eigenmaps2003In: Computer Aided Systems Theory - EUROCAST 2003: 9th International Workshop on Computer Aided Systems Theory Las Palmas de Gran Canaria, Spain, February 24-28, 2003 Revised Selected Papers / [ed] Roberto Moreno-Díaz and Franz Pichler, Springer Berlin/Heidelberg, 2003, Vol. 2809, p. 518-529Conference paper (Refereed)
    Abstract [en]

    We propose a novel post processing method for visualization of fiber traces from DT-MRI data. Using a recently proposed non-linear dimensionality reduction technique, Laplacian eigenmaps [3], we create a mapping from a set of fiber traces to a low dimensional Euclidean space. Laplacian eigenmaps constructs this mapping so that similar traces are mapped to similar points, given a custom made pairwise similarity measure for fiber traces. We demonstrate that when the low-dimensional space is the RGB color space, this can be used to visualize fiber traces in a way which enhances the perception of fiber bundles and connectivity in the human brain.

  • 97.
    Brun, Anders
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Svensson, Björn
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl-Fredrik
    Herberthson, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Mathematics, Applied Mathematics.
    Wrangsjö, Andreas
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Filtering Vector-Valued Images using Importance Sampling2007In: Proceedings of the {SSBA} Symposium on Image Analysis,2007, 2007Conference paper (Other academic)
  • 98.
    Brun, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Svensson, Björn
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Herberthson, Magnus
    Linköping University, Department of Mathematics, Applied Mathematics. Linköping University, The Institute of Technology.
    Wrangsjö, Andreas
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using Importance Sampling for Bayesian Feature Space Filtering2007In: Proceedings of the 15th Scandinavian conference on image analysis / [ed] Kjær Bjarne Ersbøll and Kim Steenstrup Pedersen, Berlin, Heidelberg: Springer-Verlag , 2007, p. 818-827Conference paper (Refereed)
    Abstract [en]

    We present a one-pass framework for filtering vector-valued images and unordered sets of data points in an N-dimensional feature space. It is based on a local Bayesian framework, previously developed for scalar images, where estimates are computed using expectation values and histograms. In this paper we extended this framework to handle N-dimensional data. To avoid the curse of dimensionality, it uses importance sampling instead of histograms to represent probability density functions. In this novel computational framework we are able to efficiently filter both vector-valued images and data, similar to e.g. the well-known bilateral, median and mean shift filters.

  • 99.
    Brun, Anders
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Westin, Carl_Fredrik
    Haker, S.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A novel approach to averaging, filtering and interpolation of 3-D object orientation data2004In: Proceedings of the Swedish Symposium on Image Analysis (2004), 2004, p. 5-8Conference paper (Other academic)
  • 100.
    Brun, Anders
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA.
    Westin, Carl-Fredrik
    Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA.
    Haker, Steven
    Laboratory of Mathematics in Imaging, Harvard Medical School, Boston, MA, USA.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    A tensor-like representation for averaging, filtering and interpolation of 3D object orientation data2005In: Image Processing, 2005. ICIP 2005. IEEE International Conference on  (Volume:3 ), 2005, p. 1092-1095Conference paper (Refereed)
    Abstract [en]

    Averaging, filtering and interpolation of 3-D object orientation data is important in both computer vision and computer graphics, for instance to smooth estimates of object orientation and interpolate between keyframes in computer animation. In this paper we present a novel framework in which the non-linear nature of these problems is avoided by embedding the manifold of 3-D orientations into a 16-dimensional Euclidean space. Linear operations performed in the new representation can be shown to be rotation invariant, and defining a projection back to the orientation manifold results in optimal estimates with respect to the Euclidean metric. In other words, standard linear filters, interpolators and estimators may be applied to orientation data, without the need for an additional machinery to handle the non-linear nature of the problems. This novel representation also provides a way to express uncertainty in 3-D orientation, analogous to the well known tensor representation for lines and hyperplanes.

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