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  • 101.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework2010In: IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2010, p. 473-476Conference paper (Refereed)
    Abstract [en]

    Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.

  • 102.
    Forsberg, Daniel
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Classification of multivariate medical datasets using deformable models - A work in progress2009Conference paper (Other academic)
    Abstract [en]

    This paper presents an overview of the project “Classification of multivariate medical datasets using deformable models” and the current work within the project. The project is a joint venture between the Department of Biomedical Engineering (Linköping University), the Center for Medical Image Science and Visualization (Linköping University) and Sectra Imtec AB (Linköping) and focuses on extending a deformable model approach, named the Morphon, to 3D and to utilize multi-variate data with multiple priors. Recent work in the project includes evaluating different methods for estimating the displacement field and automatic scale control.

  • 103.
    Forsberg, Daniel
    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, Mats
    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.
    Extending Image Registration Using Polynomial Expansion To Diffeomorphic Deformations2012Conference paper (Other academic)
    Abstract [en]

    The use of polynomial expansion in image registration has previously been shown to be beneficial due to fast convergence and high accuracy. However, earlier work has only briefly out-lined how non-rigid image registration is handled, e.g. not discussing issues like regularization of the displacement field or how to accumulate the displacement field. In this work, it is shown how non-rigid image registration based upon polynomial expansion can be integrated into a generic framework for non-rigid image registration achieving diffeomorphic displacement fields. The proposed non-rigid image registration algorithm using diffeomorphic field accumulation has been evaluated on both synthetically deformed data and real image data and compared to traditional field accumulation. The results clearly demonstrate the power of the diffeomorphic field accumulation.

  • 104.
    Forsberg, Daniel
    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, Mats
    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.
    Non-rigid Diffeomorphic Image Registration of Medical Images Using Polynomial Expansion2012In: Image Analysis and Recognition: 9th International Conference, ICIAR 2012, Aveiro, Portugal, June 25-27, 2012. Proceedings, Part II / [ed] Aurélio Campilho, Mohamed Kamel, Berlin / Heidelberg: Springer, 2012, Vol. 7325, p. 304-312Conference paper (Refereed)
    Abstract [en]

    The use of polynomial expansion in image registration has previously been shown to be beneficial due to fast convergence and high accuracy. However, earlier work has only briefly out-lined how non-rigid image registration is handled, e.g. not discussing issues like regularization of the displacement field or how to accumulate the displacement field. In this work, it is shown how non-rigid image registration based upon polynomial expansion can be integrated into a generic framework for non-rigid image registration achieving diffeomorphic displacement fields. The proposed non-rigid image registration algorithm using diffeomorphic field accumulation is evaluated on both synthetically deformed data and real image data and compared to additive field accumulation. The results clearly demonstrate the power of the diffeomorphic field accumulation.

  • 105.
    Forsberg, Daniel
    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).
    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).
    Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration2010In: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, p. 2329-2332Conference paper (Refereed)
    Abstract [en]

    Phase-based methods are commonly applied in image registration. When working with phase-difference methods only a single is employed, although the algorithms are normally iterated over multiple scales, whereas phase-congruency methods utilize the phase from multiple scales simultaneously. This paper presents an extension to phase-difference methods employing parallel scales to achieve more accurate displacements. Results are also presented clearly favouring the use of parallel scales over single scale in more than 95% of the 120 tested cases. 

  • 106.
    Forsberg, Daniel
    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.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Non-Rigid Volume Registration - A CUDA-based GPU Implementation of the Morphon2011Conference paper (Other academic)
    Abstract [en]

    Image registration is frequently used within the medical image domain and where methods with high performance are required. The need for high accuracy coupled with high speed is especially important for applications such as adaptive radiation therapy and image-guided surgery. During the last years, a number of significant projects have been introduced to make the computational power of GPUs available to a wider audience. The most well known project is the introduction of CUDA (Compute Unified Device Architecture). In this paper, we present a CUDA based GPU implementation of a non-rigid image registration algorithm, known as the Morphon, and compare it with a CPU implementation of the Morphon. The achieved speedup, in the range of 51-54x, is also compared with speedups reported from other non-rigid registration methods mplemented on the GPU. These include the Demons algorithm and a mutual information based algorithm.

  • 107.
    Forsberg, Daniel
    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.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA2011Conference paper (Other academic)
    Abstract [en]

    Image registration is a well-known concept within the medical image domain and has been shown to be useful in a number of dierent tasks. However, due to sometimes long processing times, image registration is not fully utilized in clinical workows, where time is an important factor. During the last couple of years, a number of signicant projects have been introduced to make the computational power of GPUs available to a wider audience, where the most well known is CUDA. In this paper we present, with the aid of CUDA, a speedup in the range of 38-44x (from 29 minutes to 40 seconds) when implementing a phasebased non-rigid image registration algorithm, known as the Morphon, on a single GPU. The achieved speedup is in the same magnitude as the speedups reported from other non-rigid registration algorithms fully ported to the GPU. Given the impressive speedups, both reported in this paper and other papers, we therefore consider that it is now feasible to eectively integrate image registration into various clinical workows, where time is a critical factor.

  • 108.
    Forsberg, Daniel
    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.
    Farnebäck, Gunnar
    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, Center for Medical Image Science and Visualization (CMIV). Linköping University, The Institute of Technology.
    Westin, Carl-Fredrik
    Harvard Medical School.
    Multi-modal Image Registration Using Polynomial Expansion and Mutual Information2012In: Biomedical Image Registration: Proceedings of the 5th International Workshop, WBIR 2012, Nashville, TN, USA, July 7-8, 2012 / [ed] Benoit M. Dawant, Gary E. Christensen, J.Michael Fitzpatrick and Daniel Rueckert, Springer Berlin/Heidelberg, 2012, p. 40-49Chapter in book (Refereed)
    Abstract [en]

    This book constitutes the refereed proceedings of the 5th International Workshop on Biomedical Image Registration, WBIR 2012, held in Nashville, Tennessee, USA, in July 2012. The 20 full papers and 11 poster papers included in this volume were carefully reviewed and selected from 44 submitted papers. They full papers are organized in the following topical sections: multiple image sets; brain; non-rigid anatomy; and frameworks and similarity measures.

  • 109.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden .
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Sectra, Linköping, Sweden .
    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).
    Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis2014In: Computational Methods and Clinical Applications for Spine Imaging: Proceedings of the Workshop held at the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, September 22-26, 2013, Nagoya, Japan / [ed] Jianhua Yao,Tobias Klinder, Shuo Li, Springer, 2014, Vol. 17, p. 123-134Conference paper (Refereed)
    Abstract [en]

    In this paper, we propose the concept of eigenspine, a data analysis schemeuseful for quantifying the linear correlation between different measures relevant fordescribing spinal deformities associated with spinal diseases, such as idiopathic scoliosis.The proposed concept builds upon the use of principal component analysis(PCA) and canonical correlation analysis (CCA), where PCA is used to reduce thenumber of dimensions in the measurement space, thereby providing a regularizationof the measurements, and where CCA is used to determine the linear dependence betweenpair-wise combinations of the different measures. To demonstrate the usefulnessof the eigenspine concept, the measures describing position and rotation of thelumbar and the thoracic vertebrae of 22 patients suffering from idiopathic scoliosiswere analyzed. The analysis showed that the strongest linear relationship is foundbetween the anterior-posterior displacement and the sagittal rotation of the vertebrae,and that a somewhat weaker but still strong correlation is found between thelateral displacement and the frontal rotation of the vertebrae. These results are wellin-line with the general understanding of idiopathic scoliosis. Noteworthy though isthat the obtained results from the analysis further proposes axial vertebral rotationas a differentiating measure when characterizing idiopathic scoliosis. Apart fromanalyzing pair-wise linear correlations between different measures, the method isbelieved to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

  • 110.
    Forsberg, Daniel
    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).
    Lundström, Claes
    Linköping University, Department of Science and Technology, Media and Information Technology. 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).
    Model-based registration for assessment of spinal deformities in idiopathic scoliosis2014In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, no 2, p. 311-326Article in journal (Refereed)
    Abstract [en]

    Detailed analysis of spinal deformity is important within orthopaedic healthcare, in particular for assessment of idiopathic scoliosis. This paper addresses this challenge by proposing an image analysis method, capable of providing a full three-dimensional spine characterization. The proposed method is based on the registration of a highly detailed spine model to image data from computed tomography. The registration process provides an accurate segmentation of each individual vertebra and the ability to derive various measures describing the spinal deformity. The derived measures are estimated from landmarks attached to the spine model and transferred to the patient data according to the registration result. Evaluation of the method provides an average point-to-surface error of 0.9 mm ± 0.9 (comparing segmentations), and an average target registration error of 2.3 mm ± 1.7 (comparing landmarks). Comparing automatic and manual measurements of axial vertebral rotation provides a mean absolute difference of 2.5° ± 1.8, which is on a par with other computerized methods for assessing axial vertebral rotation. A significant advantage of our method, compared to other computerized methods for rotational measurements, is that it does not rely on vertebral symmetry for computing the rotational measures. The proposed method is fully automatic and computationally efficient, only requiring three to four minutes to process an entire image volume covering vertebrae L5 to T1. Given the use of landmarks, the method can be readily adapted to estimate other measures describing a spinal deformity by changing the set of employed landmarks. In addition, the method has the potential to be utilized for accurate segmentations of the vertebrae in routine computed tomography examinations, given the relatively low point-to-surface error.

  • 111.
    Forsberg, Daniel
    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.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    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.
    Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes2011In: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, p. 592-603Conference paper (Refereed)
    Abstract [en]

    The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

  • 112.
    Forsberg, Daniel
    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.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Vavruch, Ludvig
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Neurosurgery. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Tropp, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Clinical and Experimental Medicine, Orthopaedics. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Orthopaedics in Linköping.
    Knutsson, Hans
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, Faculty of Health Sciences.
    Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis2013In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, no 6, p. 1775-1787Article in journal (Refereed)
    Abstract [en]

    Reliable measurements of spinal deformities in idiopathic scoliosis are vital, since they are used for assessing the degree of scoliosis, deciding upon treatment and monitoring the progression of the disease. However, commonly used two dimensional methods (e.g. the Cobb angle) do not fully capture the three dimensional deformity at hand in scoliosis, of which axial vertebral rotation (AVR) is considered to be of great importance. There are manual methods for measuring the AVR, but they are often time-consuming and related with a high intra- and inter-observer variability. In this paper, we present a fully automatic method for estimating the AVR in images from computed tomography. The proposed method is evaluated on four scoliotic patients with 17 vertebrae each and compared with manual measurements performed by three observers using the standard method by Aaro-Dahlborn. The comparison shows that the difference in measured AVR between automatic and manual measurements are on the same level as the inter-observer difference. This is further supported by a high intraclass correlation coefficient (0.971-0.979), obtained when comparing the automatic measurements with the manual measurements of each observer. Hence, the provided results and the computational performance, only requiring approximately 10 to 15 s for processing an entire volume, demonstrate the potential clinical value of the proposed method.

  • 113.
    Forsberg, Daniel
    et al.
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Sectra, Linköping, Sweden.
    Lundström, Claes
    Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology. Sectra, Linköping, Sweden.
    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).
    Eigenspine: Computing the Correlation between Measures Describing Vertebral Pose for Patients with Adolescent Idiopathic Scoliosis2014In: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 38, no 7, p. 549-557Article in journal (Refereed)
    Abstract [en]

    This paper describes the concept of eigenspine, a concept applicable for determining the correlation between pair-wise combinationsof measures useful for describing the three-dimensional spinal deformities associated with adolescent idiopathic scoliosis. Theproposed data analysis scheme is based upon the use of principal component analysis (PCA) and canonical correlation analysis(CCA). PCA is employed to reduce the dimensionality of the data space, thereby providing a regularization of the measurements,and CCA is employed to determine the linear dependence between pair-wise combinations of different measures. The usefulness ofthe eigenspine concept is demonstrated by analyzing the position and the rotation of all lumbar and thoracic vertebrae as obtainedfrom 46 patients suffering from adolescent idiopathic scoliosis. The analysis showed that the strongest linear relationship is foundbetween the lateral displacement and the coronal rotation of the vertebrae, and that a somewhat weaker but still strong correlationis found between the coronal rotation and the axial rotation of the vertebrae. These results are well in-line with the generalunderstanding of idiopathic scoliosis. Noteworthy though is that the correlation between the anterior-posterior displacement and thesagittal rotation was not as strong as expected and that the obtained results further indicate the need for including the axial vertebralrotation as a measure when characterizing different types of idiopathic scoliosis. Apart from analyzing pair-wise correlationsbetween different measures, the method is believed to be suitable for finding a maximally descriptive low-dimensional combinationof measures describing spinal deformities in idiopathic scoliosis.

  • 114.
    Forsberg, Daniel
    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. Sectra Imtec, Linkoping, Sweden.
    Rathi, Yogesh
    Harvard Medical School, Boston, MA, USA.
    Bouix, Sylvain
    Harvard Medical School, Boston, MA, USA.
    Wassermann, Demian
    Harvard Medical School, Boston, MA, USA.
    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.
    Westin, Carl-Fredrik
    Harvard Medical School, Boston, MA, USA.
    Improving Registration Using Multi-channel Diffeomorphic Demons Combined with Certainty Maps2011In: Multimodal Brain Image Analysis: First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Toronto, Canada, September 18, 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 7012/2011, p. 19-26Conference paper (Refereed)
    Abstract [en]

    The number of available imaging modalities increases both in clinical practice and in clinical studies. Even though data from multiple modalities might be available, image registration is typically only performed using data from a single modality. In this paper, we propose using certainty maps together with multi-channel diffeomorphic demons in order to improve both accuracy and robustness when performing image registration. The proposed method is evaluated using DTI data, multiple region overlap measures and a fiber bundle similarity metric.

  • 115.
    Friman, Ola
    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).
    Lundberg, Mikael
    Tylen, Ulf
    Department of Radioology, Göteborg University, Sweden.
    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).
    Recognizing emphysema - A neural network approach2002In: Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1) / [ed] R. Kasturi, D. Laurendeau, C. Suen, IEEE Computer Society, 2002, p. 512-515Conference paper (Refereed)
    Abstract [en]

    An accurate and fully automatic method for detecting and quantifying emphysema in CT-images is presented. The method is based on an image preprocessing step followed by a neural network classifier trained to separate true emphysema from artifacts. The proposed approach is shown to be superior to an established method when applied on real patient data.

  • 116.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lundberg, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radio Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    A Correlation Framwork For Functional Mri Data Analysis.2001In: Proceedings of SCIA 2001. Bergen,2001, 2001, p. 3-9Conference paper (Refereed)
    Abstract [en]

    A correlation framework for detecting brain activity in functional MRI data is presented. In this framework, a novel method based on canonical correlation analysis follows as a natural extension of established analysis methods. The new method shows very good detection performance. This is demonstrated by localizing brain areas which control finger movements and areas which are involved in numerical mental calculation.

  • 117.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Adaptive analysis of fMRI data2003In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 19, no 3, p. 837-845Article in journal (Refereed)
    Abstract [en]

    This article introduces novel and fundamental improvements of fMRI data analysis. Central is a technique termed constrained canonical correlation analysis, which can be viewed as a natural extension and generalization of the popular general linear model method. The concept of spatial basis filters is presented and shown to be a very successful way of adaptively filtering the fMRI data. A general method for designing suitable hemodynamic response models is also proposed and incorporated into the constrained canonical correlation approach. Results that demonstrate how each of these parts significantly improves the detection of brain activity, with a computation time well within limits for practical use, are provided.

  • 118.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Lundberg, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radiation Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
    Canonical correlation as a tool in functional MRI data analysis2001In: SSAB Symposium on Image Analysis,2001, 2001Conference paper (Other academic)
  • 119.
    Friman, Ola
    et al.
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection and detrending in fMRI data analysis2004In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 22, no 2, p. 645-655Article in journal (Refereed)
    Abstract [en]

    This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

  • 120.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection of neural activity in fMRI using maximum correlation modeling2002In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 15, no 2, p. 386-395Article in journal (Refereed)
    Abstract [en]

    A technique for detecting neural activity in functional MRI data is introduced. It is based on a novel framework termed maximum correlation modeling. The method employs a spatial filtering approach that adapts to the local activity patterns, which results in an improved detection sensitivity combined with good specificity. A spatially varying hemodynamic response is simultaneously modelled by a sum of two gamma functions. Comparisons to traditional analysis methods are made using both synthetic and real data. The results indicate that the maximum correlation modeling approach is a strong alternative for analyzing fMRI data.

  • 121.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Faculty of Health Sciences.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Exploratory fMRI analysis by autocorrelation maximization2002In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 16, no 2, p. 454-464Article in journal (Refereed)
    Abstract [en]

    A novel and computationally efficient 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 relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

  • 122.
    Friman, Ola
    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).
    Lundberg, Peter
    Linköping University, Department of Medicine and Care, Radiation Physics. Linköping University, Faculty of Health Sciences. 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).
    Hierarchical temporal blind source separation of fMRI data2002In: Proceedings of the ISMRM Annual Meeting (ISMRM'02), 2002Conference paper (Refereed)
    Abstract [en]

    Blind Source Separation (BSS) of fMRI data can be done both temporally and spatially. Temporal BSS of fMRI data has one fundamental problem not encountered in the spatial BSS approach. There are thousands of observed timecourses in an fMRI data set while the number of samples of each timecourse typically is less than two hundred. This re lation makes the problem of recovering the underlying temporal sources ill-posed. This contribution eliminates this problem by introducing a hierarchical approach for performing temporal BSS of fMRI data.

  • 123.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    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.
    Imaging Brain Function2002Conference paper (Other academic)
  • 124.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Borga, Magnus
    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.
    Tylén, U.
    Göteborgs universitet.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Emphysema Detection in CT Images2002Conference paper (Other academic)
    Abstract [en]

    This paper describes a fully automatic approach for detecting emphysema in CT im ages of the lungs. The method combines an image processing step, where potential emphysematous area s are extracted, and a neural network step trained to rec

  • 125.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Cedefamn, Jonny
    Linköping University, Department of Neuroscience and Locomotion. Linköping University, Faculty of Health Sciences.
    Lundberg, Peter
    Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics. Linköping University, Faculty of Health Sciences.
    Borga, Magnus
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Detection of neural activity in functional MRI using canonical correlation analysis2001In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 45, no 2, p. 323-330Article in journal (Refereed)
    Abstract [en]

    A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.

  • 126.
    Friman, Ola
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Lundberg, Peter
    Linköping University, Faculty of Health Sciences. Linköping University, Department of Medicine and Care, Radio Physics. Östergötlands Läns Landsting, Centre of Surgery and Oncology, Department of Radiation Physics.
    Borga, Magnus
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Cedefamn, Jonny
    Knutsson, Hans
    Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Physiological Measurements.
    Increased detection sensitivity in fMRI by adaptive filtering.2001In: Proceedings iSMRM and ESMRM meeting 2001, Glasgow,2001, 2001, p. 1209-1209Conference paper (Refereed)
  • 127.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Arvidsson, Jan
    n/a.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    GOP, A Paradigm in Hierarchical Image Processing1982In: Proceedings of The First IEEE Computer Society International Symposium on Medical Imaging and Image Interpretation, ISMI II'82: Berlin, Federal Republic of Germany, 1982Conference paper (Refereed)
  • 128.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Compact Associative Representation of Structural Information1988Report (Other academic)
  • 129.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Compact Associative Representation of Visual Information1990In: Proceedings of The 10th International Conference on Pattern Recognition: Atlantic City, New Jersey, 1990Conference paper (Refereed)
  • 130.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Contrast of Structured and Homogenous Representations1983In: Physical and Biological Processing of Images: eds O. J. Braddick and A. C. Sleigh / [ed] Oliver J. Braddick, A. C. Sleigh, Berlin: Springer Verlag , 1983, p. 282-303Chapter in book (Refereed)
  • 131.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Hierarchical Processing of Structural Information in Artificial Intelligence1982In: Proceedings of 1982 IEEE Conference on Acoustics, Speech and Signal Processing: Paris, 1982Conference paper (Refereed)
  • 132.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Hedlund, Martin
    n/a.
    Hierarchical Processing of Structural Information1981Report (Other academic)
  • 133.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Westelius, Carl-Johan
    n/a.
    Wiklund, Johan
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Issues in Robot Vision1994In: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 12, no 3, p. 131-148Article in journal (Refereed)
    Abstract [en]

    In this paper, we discuss certain issues regarding robot vision. The main theme will be the importance of the choice of information representation. We will see the implications at different parts of a robot vision structure. We deal with aspects of pre-attentive versus attentive vision, control mechanisms for low level focus of attention, and representation of motion as the orientation of hyperplanes in multdimensional time-space. Issues of scale will be touched upon, and finally, a depth-from stereo algorithm based on guadrature filter phase is presented.

  • 134.
    Granlund, Gösta H.
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Wilson, Roland
    n/a.
    Image Enhancement1983In: Fundamentals in Computer Vision: ed O. D. Faugeras / [ed] O. D. Faugeras, Cambridge: Cambridge University Press , 1983, p. 57-68Chapter in book (Refereed)
  • 135.
    Granlund, Gösta
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, HansLinköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Signal Processing for Computer Vision1995Collection (editor) (Other academic)
    Abstract [en]

    Signal Processing for Computer Vision is a unique and thorough treatment of the signal processing aspects of filters and operators for low-level computer vision.

    Computer vision has progressed considerably over recent years. From methods only applicable to simple images, it has developed to deal with increasingly complex scenes, volumes and time sequences. A substantial part of this book deals with the problem of designing models that can be used for several purposes within computer vision. These partial models have some general properties of invariance generation and generality in model generation.

    Signal Processing for Computer Vision is the first book to give a unified treatment of representation and filtering of higher order data, such as vectors and tensors in multidimensional space. Included is a systematic organisation for the implementation of complex models in a hierarchical modular structure and novel material on adaptive filtering using tensor data representation.

    Signal Processing for Computer Vision is intended for final year undergraduate and graduate students as well as engineers and researchers in the field of computer vision and image processing.

  • 136.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Özarslan, Evren
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI2019In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, article id 43Article in journal (Refereed)
    Abstract [en]

    Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.

    Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).

    Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.

    Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.

  • 137.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Nilsson, Markus
    Department of Clinical Sciences, Radiology, Lund UniversityLundSweden.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks2019In: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, p. 489-498Conference paper (Refereed)
    Abstract [en]

    Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI.

  • 138.
    Gu, Xuan
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Sidén, Per
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Wegmann, Bertil
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Eklund, Anders
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Villani, Mattias
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Science & Engineering.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Bayesian Diffusion Tensor Estimation with Spatial Priors2017In: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, p. 372-383Conference paper (Refereed)
    Abstract [en]

    Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

  • 139.
    Haglund, Leif
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Bårman, Håkan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Estimation of Velocity and Acceleration in Time Sequences1992In: Theory & Applications of Image Analysis: eds P. Johansen and S. Olsen / [ed] P. Johansen and S. Olsen, Singapore: World Scientific Publishing Co , 1992, p. 223-236Chapter in book (Refereed)
  • 140.
    Haglund, Leif
    et al.
    n/a.
    Bårman, Håkan
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Estimation of Velocity and Acceleration in Time Sequences1991In: Proceedings of the 7th Scandinavian Conference on Image Analysis: Aalborg, Denmark, 1991, p. 1033-1041Conference paper (Refereed)
  • 141.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    On Phase Representation of Image Information1989In: The 6th Scandinavian Conference on Image Analysis: Oulu, Finland, 1989, p. 1082-1089Conference paper (Refereed)
  • 142.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    On Scale and Orientation Adaptive Filtering1992In: Proceedings of the SSAB Symposium on Image Analysis: Uppsala, 1992Conference paper (Refereed)
  • 143.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
    Scale Analysis Using Phase Representation1989In: The 6th Scandinavian Conference on Image Analysis: Oulu, Finland, 1989, p. 1118-1125Conference paper (Refereed)
  • 144.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Scale and Orientation Adaptive Filtering1993In: SCIA8: Tromso, Norway, 1993Conference paper (Refereed)
    Abstract [en]

    This paper contains a presentation of a scale and orientation adaptive filtering strategy for images. The size, shape and orientation of the filter are signal controlled and thus locally adapted to each neighbourhood according to an estimated model. On each scale the filter is constructed as a linear weighting of fixed oriented bandpass filters having the same shape but different orientations. The resulting filter is interpolated from all scale levels, and spans over more than 6 octaves. It is possible to reconstruct an enhanced original image from the filtered images. The performance of the reconstruction algorithm displays two desirable but normally contradictory features, namely edge enhancement and an improvement of the signal-to-noise ratio. The adaptive filtering method has been tested on both real data and synthesized test data. The results are very good on a wide variety of images from moderate signal-to-noise ratios to low, even lower than 0 dB, SNR.

  • 145.
    Hedlund, Martin
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    A Consistency Operation for Line and Curve Enhancement1982In: The Computer Society Conference on PR&IP: Anaheim, California, 1982Conference paper (Refereed)
  • 146.
    Hedlund, Martin
    et al.
    n/a.
    Granlund, Gösta H.
    Linköping University, Department of Electrical Engineering, Computer Vision . Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Image Filtering and Relaxation Procedures using Hierarchical Models1981In: Proceedings of the 2nd Scandinavian Conference on Image Analysis: Finland, 1981Conference paper (Refereed)
  • 147.
    Hemmendorff, M.
    et al.
    Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Kronander, T.
    SECTRA, Teknikringen 2, SE-583 30 Linköping, Sweden.
    Motion compensated digital subtraction angiography1999Conference paper (Refereed)
    Abstract [en]

    Digital subtraction angiography, whether based on traditional X-ray or MR, suers from patient motion artifacts. Until now, the usual remedy is to pixel shift by hand, or in some cases performing a global pixel shift semi-automatically. This is time consuming, and cannot handle rotations or local varying deformations over the image. We have developed a fully automatic algorithm that provides for motion compensation in the presence of large local deformations. Our motion compensation is very accurate for ordinary motions, including large rotations and deformations. It does not matter if the motions are irregular over time. For most images, it takes about a second per image to get adequate accuracy. The method is based on using the phase from lter banks of quadrature lters tuned in dierent directions and frequencies. Unlike traditional methods for optical ow and correlation, our method is more accurate and less susceptible to disturbing changes in the image, e.g. a moving contrast bolus. The implications for common practice are that radiologists' time can be significantly reduced in ordinary peripheral angiographies and that the number of retakes due to large or local motion artifacts will be much reduced.

  • 148.
    Hemmendorff, 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.
    Accurate registration and motion estimation based on canonical correlation2001In: Scandinavian conference on image analysis SCIA,2001, 2001Conference paper (Other academic)
  • 149.
    Hemmendorff, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Kronander, T.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
    Phase-based Multidimensional Volume Registration2000Conference paper (Refereed)
    Abstract [en]

    We present a method for accurate image registration and motion estimation in multidimensional volumes, such as 3D CT and MR images. The method is based on phase from quadrature filters, which makes it insensitive to variations in luminance and other disturbance in the images. The theory is not restricted to any particular kind of motion model or number of dimensions. Experimental results for affine motions in 3D show high accuracy.

  • 150.
    Hemmendorff, Magnus
    et al.
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Andersson, Mats
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Kronander, Torbjörn
    SECTRA AB, Linköping, Sweden.
    Knutsson, Hans
    Linköping University, Department of Biomedical Engineering. Linköping University, The Institute of Technology.
    Phase-based multidimensional volume registration2002In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 21, no 12, p. 1536-1543Article in journal (Refereed)
    Abstract [en]

    We present a method for accurate image registration and motion compensation in multidimensional signals, such as two-dimensional (2-D) X-ray images and three-dimensional (3-D) computed tomography/magnetic resonance imaging volumes. The method is based on phase from quadrature filters, which makes it robust to noise and temporal intensity variations. The method is equally applicable to signals of two, three or higher number of dimensions. We use parametric models, e.g., affine models, finite elements or local affine models with global regularization. Experimental results show high accuracy for 2-D and 3-D motion compensation.

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