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  • 1.
    Parraga, Adriane
    et al.
    Dept. of Electrical Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, Porto Alegre, Brazil and Dept. of Electrical Engineering, Universit´e Catholique de Louvain, Belgium.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Susin, Altamiro
    Dept. of Electrical Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, Porto Alegre, Brazil.
    De Craene, Mathieu
    Dept. of Technology, Pompeu Fabra University, Barcelona, Spain.
    Marq, Benoît
    Dept. of Electrical Engineering, Universit´e Catholique de Louvain, Belgium.
    Non-rigid registration methods assessment of 3D CT images for head-neck radiotherapy2007In: Medical Imaging 2007: Image Processing / [ed] Josien P. W. Pluim; Joseph M. Reinhardt, SPIE - International Society for Optical Engineering, 2007, p. 6512H1-6512H9Conference paper (Other academic)
    Abstract [en]

    Intensity Modulated Radiotherapy is a new technique enabling the sculpting of the 3D radiation dose. It enables to modulate the delivery of the dose inside the malignant areas and constrain the radiation plan for protecting important functional areas. It also raises the issues of adequacy and accuracy of the selection and delineation of the target volumes. The delineation in the patient image of the tumor volume is highly time-consuming and requires considerable expertise. In this paper we focus on atlas based automatic segmentation of head and neck patients and compare two non-rigid registration methods: B-Spline and Morphons. To assess the quality of each method, we took a set of four 3D CT patient's images previously segmented by a doctor with the organs at risk. After a preliminary affine registration, both non-rigid registration algorithms were applied to match the patient and atlas images. Each deformation field, resulted from the non-rigid deformation, was applied on the masks corresponding to segmented regions in the atlas. The atlas based segmentation masks were compared to manual segmentations performed by an expert. We conclude that Morphons has performed better for matching all structures being considered, improving in average 11% the segmentation.

  • 2.
    Parraga, Adriane
    et al.
    UFRGS, Dept. Electrical Engineering, Porto Alegre/RS, Brazil.
    Susin, Altamiro
    UFRGS, Dept. Electrical Engineering, Porto Alegre/RS, Brazil.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Marq, Benoit
    Universit´e Catholique de Louvain, Communication and Rem. Sen. lab., Louvain-la-Neuve, Belgium.
    De Craene, Mathieu
    Universit´e Catholique de Louvain, Communication and Rem. Sen. lab., Louvain-la-Neuve, Belgium.
    Quality Assessment of Non-rigid Registration Methods for Atlas-based Segmentation in Head-neck Radiotherapy2007In: ICASSP 2007, IEEE , 2007, p. I-445-I-448Conference paper (Refereed)
    Abstract [en]

    In this paper we compare three non-rigid registration methods for atlas-based segmentation: B-splines, morphons and a combination of morphons and demons. To assess the quality of each method, we use a data set of four patients, containing for each patient the computed tomography (CT) image and a manual segmentation of the organs at risk performed by an expert of the head and neck anatomy. Non-rigid registration algorithms have been used to match the patient and atlas images. Each deformation field, resulting from the non-rigid deformation, have been applied on the masks corresponding to segmented regions in the atlas. The atlas based segmented masks have been compared to manual segmentations performed by the expert. The results show that the combined method (morphons + demons) achieves the best performances on this dataset resulting in an average improvement of 6% with respect to morphons and 18% with respect to B-spline.

  • 3.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Automatic generation of patient specific models for hip surgery simulation2006Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Simulation of surgical procedures in computer based simulator systems is a relatively new area of interest in the field of medical technology. Many different systems are under development, but it is still far until this technique becomes routine in a clinical environment.

    One limitation with these systems is that the computer generated models of the anatomy are ususally not patient specific. In general these systems contain one or a couple of generic models, reflecting the normal anatomy. In order to use these systems as tools for preoperative planning, it must be possible to generate computer models from real patient cases. This is relevant also when these systems are used for training and education, since the surgeon can practice on a variety of real cases, ranging from routine procedures to more complex, or more unusual, tasks.

    To produce patient specific models a segmentation process is generally involved. A manual segmentation method, where a user delineates the interesting structure slice by slice in a patient dataset, is a very tedious process, and the result tends to vary between different users. The results would be more consistent, and the process would be more time and cost efficient, if a computer performed this task automatically, with a limited amount of user interaction.

    This work focus on automatic segmentation of hip bones from computer tomography scans, in order to generate patient specific computer models for a hip fracture surgery simulator. The segmentation is carried out by matching a template dataset to a patient dataset using a method called the morphon method. Each point in the template is categorised as one of three tissue types, soft tissue (background), spongy bone (bone interior), or cortical bone (bone surface). When this dataset has been matched to the patient dataset the information in the template can be projected onto the patient dataset and used to find the different objects in this dataset.

    The presented method can be used in other applications as well. One example demonstrated in this thesis is segmentation of a structure in the brain called the hippocampus.

    List of papers
    1. Non-rigid registration using morphons
    Open this publication in new window or tab >>Non-rigid registration using morphons
    2005 (English)In: Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings / [ed] Heikki Kalviainen, Jussi Parkkinen and Arto Kaarna, Springer Berlin/Heidelberg, 2005, Vol. 3540, p. 501-510Chapter in book (Refereed)
    Abstract [en]

    The Morphon, a non-rigid registration method is presented and applied to a number of registration applications. The algorithm takes a prototype image (or volume) and morphs it into a target image using an iterative, multi-resolution technique. The deformation process is done in three steps: displacement estimation, deformation field accumulation and deformation. The framework could be described in very general terms, but in this paper we focus on a specific implementation of the Morphon framework. The method can be employed in a wide range of registration tasks, which is shown in four very different registration examples, 2D photographs of hands and faces, 3D CT data of the hip region, and 3D MR brain images.

    Place, publisher, year, edition, pages
    Springer Berlin/Heidelberg, 2005
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 3540
    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-48191 (URN)10.1007/11499145_51 (DOI)978-3-540-26320-3 (ISBN)978-3-540-31566-7 (ISBN)3-540-26320-9 (ISBN)
    Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2018-02-08Bibliographically approved
    2. A hip surgery simulator based on patient specific models generated by automatic segmentation
    Open this publication in new window or tab >>A hip surgery simulator based on patient specific models generated by automatic segmentation
    2006 (English)In: Medicine Meets Virtual Reality 14: Accelerating Change in Healthcare: Next Medical Toolkit / [ed] James D Westwood; et al, Amsterdam, Nederländerna: IOS Press, 2006, p. 431-436Conference paper, Published paper (Refereed)
    Abstract [en]

    The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.

    Place, publisher, year, edition, pages
    Amsterdam, Nederländerna: IOS Press, 2006
    Series
    Studies in Health Technology and Informatics, ISSN 0926-9630 ; 119
    Keyword
    Surgery simulation, hip, patient specific models, segmentation, registration, morphon
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-32716 (URN)000269690200092 ()18638 (Local ID)978-1-58603-583-9 (ISBN)18638 (Archive number)18638 (OAI)
    Conference
    Medicine Meets Virtual Reality Conference MMVR06,2006
    Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2015-10-08Bibliographically approved
    3. Non-rigid registration for automatic fracture segmentation
    Open this publication in new window or tab >>Non-rigid registration for automatic fracture segmentation
    2006 (English)In: Image Processing, 2006, Atlanta: IEEE , 2006, p. 1185-1188Conference paper, Published paper (Refereed)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model-based non-rigid registration methods. These algorithms work well when the images do not contain any large deviations from the normal anatomy. We have previously used such a method to generate patient specific models of hip bones for surgery simulation. The method that was used, the morphon method, registers two-or three-dimensional images using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image using quadrature filter phase difference to estimate the local displacement. The morphon method has in this work been extended to deal with automatic segmentation of fractured bones. Two features have been added. First, the method is modified such that multiple prototypes (in this case two) can be used. Second, normalised convolution is utilized for the displacement estimation, to guide the registration of the second prototype, based on the result of the registration of the first one

    Place, publisher, year, edition, pages
    Atlanta: IEEE, 2006
    Series
    International Conference on Image Processing. Proceedings, ISSN 1522-4880
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-34136 (URN)10.1109/ICIP.2006.312695 (DOI)000245768500297 ()20910 (Local ID)1-4244-0480-0 (ISBN)e-1-4244-0481-9 (ISBN)20910 (Archive number)20910 (OAI)
    Conference
    IEEE International Conference on Image Processing (ISIP 2006), 8-11 October 2006, Atlanta, GA, USA
    Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2015-10-09
    4. Automatic segmentation of CT and MR volume data using non-rigid morphon registration
    Open this publication in new window or tab >>Automatic segmentation of CT and MR volume data using non-rigid morphon registration
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model based non-rigid registration methods. The morphon algorithm is one such method. In this algorithm, two or three dimensional images are registered using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image, using local phase difference to estimate the displacement between the images. This method has been extended with normalised convolution, to guide the registration process using prior knowledge about the target image. By defining a certainty mask used in the normalised convolution one can either specify a region of interest in the target image, or, on the other hand, occlude regions in the image that should not affect the matching. Two different applications are presented, which each demonstrates the use of this method for automatic segmentation.

    National Category
    Engineering and Technology
    Identifiers
    urn:nbn:se:liu:diva-102004 (URN)
    Available from: 2013-11-26 Created: 2013-11-26 Last updated: 2014-10-08
  • 4.
    Pettersson, Johanna
    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).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Andersson, Mats
    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).
    Volume morphing for segmentation of bone from 3D data2005In: Symposium on Image Analysis SSBA,2005, 2005, p. 89-92Conference paper (Other academic)
  • 5.
    Pettersson, Johanna
    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).
    Borga, Magnus
    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).
    Some issues on the segmentation of the femur in CT data2004In: Proceedings of the Swedish Symposium on Image Analysis (2004), 2004, p. 158-161Conference paper (Other academic)
    Abstract [en]

    This paper presents a recently started project which goal is to automatically generate patient specific models for visual and haptic simulation of hip fracture surgery. It includes a preliminary study of a computed tomography (CT) dataset of the pelvic region. The paper emphasizes some issues encountered when segmenting bones in this region, especially in the area around the proximal femur.

  • 6.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Automatic hip bone segmentation using non-rigid registration2006In: 18th International Conference on Pattern Recognition, 2006, ICPR 2006 (Volume:3 ), IEEE Computer Society, 2006, p. 946-949Conference paper (Refereed)
    Abstract [en]

    This paper presents a method for automatic segmentation of bone from volumetric computed tomography (CT) data. Due to osteoporosis, which degenerates the bone density and hence decreases the intensity of the bone in the CT dataset, it is not possible to use conventional thresholding techniques to handle the segmentation. Furthermore we want to use prior knowledge about shapes and relations of the bones in the area of interest to be able to e.g. separate adjoining bones from each other. The method we suggest is the morphon algorithm in Knutsson and Andersson (2005). This is a non-rigid registration technique where an 2D or 3D image is iteratively deformed to match the corresponding structure in a target image. The method uses difference in local quadrature phase and certainty measures to estimate the deformations

  • 7.
    Pettersson, Johanna
    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).
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Generation of patient specific bone models from volume data using morphons2005In: IFMBE Proceedings: NBC'05 13th Nordic Baltic Conference Biomedical Engineering and Medical Physics / [ed] Ronnie Lundström, Britt Andersson, Helena Grip, Umeå: IFMBE , 2005, p. 199-200Conference paper (Refereed)
    Abstract [en]

    The use of simulator systems for surgical planning and training is growing as the systems become more advanced. One important feature of these systems is the possibility to work on real patient data. This paper presents a method for generating patient-specific models of the femoral bone and the pelvis to be used in a hip surgery simulator. The bones are segmented from volumetric CT data using the Morphon method [3], where a prototype pattern is iteratively morphed to fit the corresponding structure in the input data. 

  • 8.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Non-rigid registration for automatic fracture segmentation2006In: Image Processing, 2006, Atlanta: IEEE , 2006, p. 1185-1188Conference paper (Refereed)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model-based non-rigid registration methods. These algorithms work well when the images do not contain any large deviations from the normal anatomy. We have previously used such a method to generate patient specific models of hip bones for surgery simulation. The method that was used, the morphon method, registers two-or three-dimensional images using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image using quadrature filter phase difference to estimate the local displacement. The morphon method has in this work been extended to deal with automatic segmentation of fractured bones. Two features have been added. First, the method is modified such that multiple prototypes (in this case two) can be used. Second, normalised convolution is utilized for the displacement estimation, to guide the registration of the second prototype, based on the result of the registration of the first one

  • 9.
    Pettersson, Johanna
    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).
    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).
    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).
    Normalised convolution and morphons for non-rigid registration2006In: SSBA Symposium on Image Analysis,2006, 2006, p. 61-65Conference paper (Other academic)
  • 10.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Segmentation and registration with the Morphon method, four different applications2007In: Proceedings of the {SSBA} Symposium on Image Analysis,2007, 2007Conference paper (Other academic)
    Abstract [en]

    The Morphon method has shown to be a useful non-rigid registration method since it was first presented in 2005. This paper demonstrates how the method has been adapted for four different applications; hip fracture segmentation from CT data, hippocampus segmentation from MR data and registration the prostate and the head-neck region from CT data for radiotherapy planning.

  • 11.
    Pettersson, Johanna
    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. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Malmgren, Helge
    Department of Philosophy, Göteborg University, Sweden.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Automatic segmentation of CT and MR volume data using non-rigid morphon registrationManuscript (preprint) (Other academic)
    Abstract [en]

    Automatic segmentation of anatomical structures is often performed using model based non-rigid registration methods. The morphon algorithm is one such method. In this algorithm, two or three dimensional images are registered using a multi-resolution deformation scheme. A prototype image is iteratively registered to a target image, using local phase difference to estimate the displacement between the images. This method has been extended with normalised convolution, to guide the registration process using prior knowledge about the target image. By defining a certainty mask used in the normalised convolution one can either specify a region of interest in the target image, or, on the other hand, occlude regions in the image that should not affect the matching. Two different applications are presented, which each demonstrates the use of this method for automatic segmentation.

  • 12.
    Pettersson, Johanna
    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.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Nordqvist, Per
    Melerit Medical AB .
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    A hip surgery simulator based on patient specific models generated by automatic segmentation2006In: Medicine Meets Virtual Reality 14: Accelerating Change in Healthcare: Next Medical Toolkit / [ed] James D Westwood; et al, Amsterdam, Nederländerna: IOS Press, 2006, p. 431-436Conference paper (Refereed)
    Abstract [en]

    The use of surgical simulator systems for education and preoperative planning is likely to increase in the future. A natural course of development of these systems is to incorporate patient specific anatomical models. This step requires some kind of segmentation process in which the different anatomical parts are extracted. Anatomical datasets are, however, usually very large and manual processing would be too demanding. Hence, automatic, or semi-automatic, methods to handle this step are required. The framework presented in this paper uses nonrigid registration, based on the morphon method, to automatically segment the hip anatomy and generate models for a hip surgery simulator system.

  • 13.
    Pettersson, Johanna
    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.
    Palmerius, Karljohan
    Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Wahlström, Ola
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Tillander, Bo
    Linköping University, Department of Clinical and Experimental Medicine. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Simulation of Patient Specific Cervical Hip Fracture Surgery With a Volume Haptic Interface2008In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 4, p. 1255-1265Article in journal (Refereed)
    Abstract [en]

    The interest for surgery simulator systems with anatomical models generated from authentic patient data is growing as these systems evolve.With access to volumetric patient data, e.g., from a computer tomography scan, haptic and visual feedback can be created directly from this dataset. This opens the door for patient specific simulations. Hip fracture surgery is one area where simulator systems is useful to train new surgeons and plan operations. To simulate the drilling procedure in this type of surgery, a repositioning of the fractured bone into correct position is first needed. This requires a segmentation process in which the bone segments are identified and the position of the dislocated part is determined. The segmentation must be automatic to cope with the large amount of data from the computer tomography scan. This work presents the first steps in the development of a hip fracture surgery simulation with patient specific models. Visual and haptic feedback is generated from the computer tomography data by simulating fluoroscopic images and the drilling process. We also present an automatic segmentation method to identify the fractured bone and determine the dislocation. This segmentation method is based on nonrigid registration with the Morphon method.

  • 14.
    Rodríguez-Vila, Borja
    et al.
    Bioengineering and Telemedicine Group, Universidad Politécnica de Madrid Spain.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    García-Vicente, Feliciano
    Medical Physics, Radiotherapy Department, University Hospital La Princesa Spain.
    Gómez, Enrique J.
    Bioengineering and Telemedicine Group, Universidad Politécnica de Madrid Spain.
    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).
    3D deformable registration for monitoring radiotherapy treatment in prostate cancer2007In: Image Analysis: 15th Scandinavian Conference, SCIA 2007, Aalborg, Denmark, June 10-14, 2007, Berlin/Heidelberg, Germany: Springer Berlin/Heidelberg, 2007, p. 750-759Conference paper (Refereed)
    Abstract [en]

    Two deformable registration methods, the Demons and the Morphon algorithms, have been used for registration of CT datasets to evaluate their usability in radiotherapy planning for prostate cancer. These methods were chosen because they can perform deformable registration in a fully automated way. The experiments show that for intrapatient registration both of the methods give useful results, although some differences exist in the way they deform the template. The Morphon method has, however, some advantageous compared to the Demons method. It is invariant to the image intensity and it does not distort the deformed data. The conclusion is therefore to recommend the Morphon method as a registration tool for this application. A more flexible regularization model is needed, though, in order to be able to catch the full range of deformations required to match the datasets.

  • 15.
    Rydell, Joakim
    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.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Pettersson, Johanna
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Johansson, Andreas
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Farnebäck, Gunnar
    Linköping University, Department of Biomedical Engineering. 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, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Department of Medicine and Care, Medical Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Nyström, Fredrik
    Linköping University, Department of Medical and Health Sciences, Internal Medicine. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase Sensitive Reconstruction for Water/Fat Separation in MR Imaging Using Inverse Gradient2007In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007. 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sebastien Ourselin and Anthony Maeder, Springer Berlin/Heidelberg, 2007, p. 210-218Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.

  • 16.
    Wrangsjö, Andreas
    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.
    Pettersson, Johanna
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Non-rigid registration using morphons2005In: Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19-22, 2005. Proceedings / [ed] Heikki Kalviainen, Jussi Parkkinen and Arto Kaarna, Springer Berlin/Heidelberg, 2005, Vol. 3540, p. 501-510Chapter in book (Refereed)
    Abstract [en]

    The Morphon, a non-rigid registration method is presented and applied to a number of registration applications. The algorithm takes a prototype image (or volume) and morphs it into a target image using an iterative, multi-resolution technique. The deformation process is done in three steps: displacement estimation, deformation field accumulation and deformation. The framework could be described in very general terms, but in this paper we focus on a specific implementation of the Morphon framework. The method can be employed in a wide range of registration tasks, which is shown in four very different registration examples, 2D photographs of hands and faces, 3D CT data of the hip region, and 3D MR brain images.

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