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  • 101.
    Eriksson-Bylund, Nina
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Ressner, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Reverberation Reduction Using 3D Wiener Filtering2003Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    One of the most common artifacts in ultrasound imaging is reverberations. These are multiple reflection echoes that register as coming from a deeper region than the depth of the interface that are causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a three dimensional (2D + time) Wiener filter has been developed. Two sequences of iq-data, the least processed signal possible to retrieve from the ultrasound system (Vingmed System Five), have been used to test the method: One sequence on a tissue-mimicking agar gel phantom in which bars of glass simulating ribs give rise to reverberations, and one sequence on an open-chest pig with a strong reverberation from a water-filled rubber glove used as a medium between the heart and the transducer. The procedure works as follows: In a graphic interface the operator is shown the image sequence. In one of the frames two areas must be marked out; One area which contains a typical reverberation artifact, and one area which will represent an artifact free signal. After creating the three dimensional Wiener filter post-processing of the sequence is performed. The developed method significantly reduced the magnitude of the reverberation artifact in the tested sequences.

  • 102.
    Farnebäck, Gunnar
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Granlund, Gösta
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Detection of point-shaped targets1996Rapport (Övrigt vetenskapligt)
    Abstract [en]

    This report documents work done at the request of the Swedish Defense Research Establishment. The studied problem is that of detecting point-shaped targets, i.e. targets whose only significant property is that of being very small, in a cluttered environment. Three approaches to the problem have been considered. The first one, based on motion compensation, was rejected at an early stage due to expected problems with robustness and computational demands. The second method, based on background modeling with principal components, turned out successful and has been studied in depth, including discussion of various extensions and improvements of the presented algorithm. Finally, a Wiener filter approach has also turned out successful, including an approximation with separable filters. The methods have been tested on sequences obtained by an IR sensor. While both the two latter approaches work well on the test sequences, the Wiener filter is simpler and computationally less expensive than the background modeling. On the other hand, the background modeling is likely to have better possibilities for extensions and improvements.

  • 103.
    Farnebäck, Gunnar
    et al.
    Linköpings universitet, Institutionen för systemteknik. Linköpings universitet, Tekniska högskolan.
    Rydell, Joakim
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Ebbers, Tino
    Linköpings universitet, Institutionen för medicin och hälsa, Klinisk fysiologi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Hjärt- och Medicincentrum, Fysiologiska kliniken US.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Efficient computation of the inverse gradient on irregular domains2007Ingår i: 2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, IEEE , 2007, s. 2710-2717Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    The inverse gradient problem, finding a scalar field f with a gradient near a given vector field g on some bounded and connected domain Omega epsilon R(n), can be solved by means of a Poisson equation with inhomogeneous Neumann boundary conditions. We present an elementary derivation of this partial differential equation and an efficient multigrid-based method to numerically compute the inverse gradient on non-rectangular domains. The utility of the method is demonstrated by a range of important medical applications such as phase unwrapping, pressure computation, inverse deformation fields, and fiber bundle tracking.

  • 104.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Adaptive Anisotropic Regularization of Deformation Fields for Non-Rigid Registration2010Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 105.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Adaptive anisotropic regularization of deformation fields for non-rigid registration using the Morphon framework2010Ingår i: IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2010, s. 473-476Konferensbidrag (Refereegranskat)
    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.

  • 106.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Classification of multivariate medical datasets using deformable models - A work in progress2009Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 107.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Extending Image Registration Using Polynomial Expansion To Diffeomorphic Deformations2012Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 108.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Non-rigid Diffeomorphic Image Registration of Medical Images Using Polynomial Expansion2012Ingår i: 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, s. 304-312Konferensbidrag (Refereegranskat)
    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.

  • 109.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Parallel Scales for More Accurate Displacement Estimation in Phase-Based Image Registration2010Ingår i: Pattern Recognition (ICPR), 2010, IEEE Computer Society, 2010, s. 2329-2332Konferensbidrag (Refereegranskat)
    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. 

  • 110.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Non-Rigid Volume Registration - A CUDA-based GPU Implementation of the Morphon2011Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 111.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Phase-Based Non-Rigid 3D Image Registration - From Minutes to Seconds Using CUDA2011Konferensbidrag (Övrigt vetenskapligt)
    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.

  • 112.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Farnebäck, Gunnar
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Tekniska högskolan.
    Westin, Carl-Fredrik
    Harvard Medical School.
    Multi-modal Image Registration Using Polynomial Expansion and Mutual Information2012Ingår i: 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, s. 40-49Kapitel i bok, del av antologi (Refereegranskat)
    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.

  • 113.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Sectra, Linköping, Sweden .
    Lundström, Claes
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan. Sectra, Linköping, Sweden .
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Eigenspine: Eigenvector Analysis of Spinal Deformities in Idiopathic Scoliosis2014Ingår i: 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, s. 123-134Konferensbidrag (Refereegranskat)
    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.

  • 114.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundström, Claes
    Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Andersson, Mats
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Model-based registration for assessment of spinal deformities in idiopathic scoliosis2014Ingår i: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 59, nr 2, s. 311-326Artikel i tidskrift (Refereegranskat)
    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.

  • 115.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Lundström, Claes
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Model-Based Transfer Functions for Efficient Visualization of Medical Image Volumes2011Ingår i: Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings, Springer Berlin/Heidelberg, 2011, Vol. 6688/2011, s. 592-603Konferensbidrag (Refereegranskat)
    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.

  • 116.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Lundström, Claes
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan.
    Andersson, Mats
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Vavruch, Ludvig
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Neurokirurgi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Ortopedkliniken i Linköping.
    Tropp, Hans
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för klinisk och experimentell medicin, Ortopedi. Linköpings universitet, Hälsouniversitetet. Östergötlands Läns Landsting, Centrum för kirurgi, ortopedi och cancervård, Ortopedkliniken i Linköping.
    Knutsson, Hans
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Hälsouniversitetet.
    Fully automatic measurements of axial vertebral rotation for assessment of spinal deformity in idiopathic scoliosis2013Ingår i: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 58, nr 6, s. 1775-1787Artikel i tidskrift (Refereegranskat)
    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.

  • 117.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Sectra, Linköping, Sweden.
    Lundström, Claes
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska högskolan. Sectra, Linköping, Sweden.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Eigenspine: Computing the Correlation between Measures Describing Vertebral Pose for Patients with Adolescent Idiopathic Scoliosis2014Ingår i: Computerized Medical Imaging and Graphics, ISSN 0895-6111, E-ISSN 1879-0771, Vol. 38, nr 7, s. 549-557Artikel i tidskrift (Refereegranskat)
    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.

  • 118.
    Forsberg, Daniel
    et al.
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. 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
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Westin, Carl-Fredrik
    Harvard Medical School, Boston, MA, USA.
    Improving Registration Using Multi-channel Diffeomorphic Demons Combined with Certainty Maps2011Ingår i: 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, s. 19-26Konferensbidrag (Refereegranskat)
    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.

  • 119.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundberg, Mikael
    Tylen, Ulf
    Department of Radioology, Göteborg University, Sweden.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Recognizing emphysema - A neural network approach2002Ingår i: Pattern Recognition, 2002. Proceedings. 16th International Conference on  (Volume:1) / [ed] R. Kasturi, D. Laurendeau, C. Suen, IEEE Computer Society, 2002, s. 512-515Konferensbidrag (Refereegranskat)
    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.

  • 120.
    Friman, Ola
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    Borga, Magnus
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    Lundberg, Peter
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Radiofysik. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    A Correlation Framwork For Functional Mri Data Analysis.2001Ingår i: Proceedings of SCIA 2001. Bergen,2001, 2001, s. 3-9Konferensbidrag (Refereegranskat)
    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.

  • 121.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Linköpings universitet, Hälsouniversitetet.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Adaptive analysis of fMRI data2003Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 19, nr 3, s. 837-845Artikel i tidskrift (Refereegranskat)
    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.

  • 122.
    Friman, Ola
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    Borga, Magnus
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    Lundberg, Peter
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiofysik. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen.
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
    Canonical correlation as a tool in functional MRI data analysis2001Ingår i: SSAB Symposium on Image Analysis,2001, 2001Konferensbidrag (Övrigt vetenskapligt)
  • 123.
    Friman, Ola
    et al.
    Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Detection and detrending in fMRI data analysis2004Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 22, nr 2, s. 645-655Artikel i tidskrift (Refereegranskat)
    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.

  • 124.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Detection of neural activity in fMRI using maximum correlation modeling2002Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 15, nr 2, s. 386-395Artikel i tidskrift (Refereegranskat)
    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.

  • 125.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Linköpings universitet, Hälsouniversitetet.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Exploratory fMRI analysis by autocorrelation maximization2002Ingår i: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 16, nr 2, s. 454-464Artikel i tidskrift (Refereegranskat)
    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.

  • 126.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Lundberg, Peter
    Linköpings universitet, Institutionen för medicin och vård, Medicinsk radiofysik. Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Hierarchical temporal blind source separation of fMRI data2002Ingår i: Proceedings of the ISMRM Annual Meeting (ISMRM'02), 2002Konferensbidrag (Refereegranskat)
    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.

  • 127.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Imaging Brain Function2002Konferensbidrag (Övrigt vetenskapligt)
  • 128.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Lundberg, Peter
    Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för medicin och hälsa, Medicinsk radiologi. Linköpings universitet, Hälsouniversitetet.
    Tylén, U.
    Göteborgs universitet.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Emphysema Detection in CT Images2002Konferensbidrag (Övrigt vetenskapligt)
    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

  • 129.
    Friman, Ola
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Cedefamn, Jonny
    Linköpings universitet, Institutionen för nervsystem och rörelseorgan. Linköpings universitet, Hälsouniversitetet.
    Lundberg, Peter
    Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen. Linköpings universitet, Hälsouniversitetet.
    Borga, Magnus
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik. Linköpings universitet, Tekniska högskolan.
    Detection of neural activity in functional MRI using canonical correlation analysis2001Ingår i: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 45, nr 2, s. 323-330Artikel i tidskrift (Refereegranskat)
    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.

  • 130.
    Friman, Ola
    et al.
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Lundberg, Peter
    Linköpings universitet, Hälsouniversitetet. Linköpings universitet, Institutionen för medicin och vård, Radiofysik. Östergötlands Läns Landsting, Kirurgi- och onkologicentrum, Radiofysikavdelningen.
    Borga, Magnus
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Cedefamn, Jonny
    Knutsson, Hans
    Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Fysiologisk mätteknik.
    Increased detection sensitivity in fMRI by adaptive filtering.2001Ingår i: Proceedings iSMRM and ESMRM meeting 2001, Glasgow,2001, 2001, s. 1209-1209Konferensbidrag (Refereegranskat)
  • 131.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Arvidsson, Jan
    n/a.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    GOP, A Paradigm in Hierarchical Image Processing1982Ingår i: Proceedings of The First IEEE Computer Society International Symposium on Medical Imaging and Image Interpretation, ISMI II'82: Berlin, Federal Republic of Germany, 1982Konferensbidrag (Refereegranskat)
  • 132.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Compact Associative Representation of Structural Information1988Rapport (Övrigt vetenskapligt)
  • 133.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Compact Associative Representation of Visual Information1990Ingår i: Proceedings of The 10th International Conference on Pattern Recognition: Atlantic City, New Jersey, 1990Konferensbidrag (Refereegranskat)
  • 134.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Contrast of Structured and Homogenous Representations1983Ingår i: 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, s. 282-303Kapitel i bok, del av antologi (Refereegranskat)
  • 135.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Hierarchical Processing of Structural Information in Artificial Intelligence1982Ingår i: Proceedings of 1982 IEEE Conference on Acoustics, Speech and Signal Processing: Paris, 1982Konferensbidrag (Refereegranskat)
  • 136.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Hedlund, Martin
    n/a.
    Hierarchical Processing of Structural Information1981Rapport (Övrigt vetenskapligt)
  • 137.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    Westelius, Carl-Johan
    n/a.
    Wiklund, Johan
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Issues in Robot Vision1994Ingår i: Image and Vision Computing, ISSN 0262-8856, E-ISSN 1872-8138, Vol. 12, nr 3, s. 131-148Artikel i tidskrift (Refereegranskat)
    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.

  • 138.
    Granlund, Gösta H.
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Wilson, Roland
    n/a.
    Image Enhancement1983Ingår i: Fundamentals in Computer Vision: ed O. D. Faugeras / [ed] O. D. Faugeras, Cambridge: Cambridge University Press , 1983, s. 57-68Kapitel i bok, del av antologi (Refereegranskat)
  • 139.
    Granlund, Gösta
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, HansLinköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Signal Processing for Computer Vision1995Samlingsverk (redaktörskap) (Övrigt vetenskapligt)
    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.

  • 140.
    Gu, Xuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Repeated Tractography of a Single Subject: How High Is the Variance?2017Ingår i: Modeling, Analysis, and Visualization of Anisotropy / [ed] Thomas Schultz, Evren Özarslan, Ingrid Hotz, Springer, 2017, s. 331-354Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.

  • 141.
    Gu, Xuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Özarslan, Evren
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI2019Ingår i: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 13, artikel-id 43Artikel i tidskrift (Refereegranskat)
    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.

  • 142.
    Gu, Xuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Nilsson, Markus
    Department of Clinical Sciences, Radiology, Lund UniversityLundSweden.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Generating Diffusion MRI Scalar Maps from T1 Weighted Images Using Generative Adversarial Networks2019Ingår i: Image Analysis: Lecture Notes in Computer Science / [ed] Felsberg M., Forssén PE., Sintorn IM., Unger J., Springer Publishing Company, 2019, s. 489-498Konferensbidrag (Refereegranskat)
    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.

  • 143.
    Gu, Xuan
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Sidén, Per
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Wegmann, Bertil
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Eklund, Anders
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Villani, Mattias
    Linköpings universitet, Institutionen för datavetenskap, Statistik och maskininlärning. Linköpings universitet, Tekniska fakulteten.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
    Bayesian Diffusion Tensor Estimation with Spatial Priors2017Ingår i: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, s. 372-383Konferensbidrag (Refereegranskat)
    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.

  • 144.
    Haglund, Leif
    et al.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Bårman, Håkan
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Estimation of Velocity and Acceleration in Time Sequences1992Ingår i: Theory & Applications of Image Analysis: eds P. Johansen and S. Olsen / [ed] P. Johansen and S. Olsen, Singapore: World Scientific Publishing Co , 1992, s. 223-236Kapitel i bok, del av antologi (Refereegranskat)
  • 145.
    Haglund, Leif
    et al.
    n/a.
    Bårman, Håkan
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Estimation of Velocity and Acceleration in Time Sequences1991Ingår i: Proceedings of the 7th Scandinavian Conference on Image Analysis: Aalborg, Denmark, 1991, s. 1033-1041Konferensbidrag (Refereegranskat)
  • 146.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Granlund, Gösta H.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    On Phase Representation of Image Information1989Ingår i: The 6th Scandinavian Conference on Image Analysis: Oulu, Finland, 1989, s. 1082-1089Konferensbidrag (Refereegranskat)
  • 147.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Granlund, Gösta H.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    On Scale and Orientation Adaptive Filtering1992Ingår i: Proceedings of the SSAB Symposium on Image Analysis: Uppsala, 1992Konferensbidrag (Refereegranskat)
  • 148.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Granlund, Gösta H.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Scale Analysis Using Phase Representation1989Ingår i: The 6th Scandinavian Conference on Image Analysis: Oulu, Finland, 1989, s. 1118-1125Konferensbidrag (Refereegranskat)
  • 149.
    Haglund, Leif
    et al.
    n/a.
    Knutsson, Hans
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Granlund, Gösta H.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Scale and Orientation Adaptive Filtering1993Ingår i: SCIA8: Tromso, Norway, 1993Konferensbidrag (Refereegranskat)
    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.

  • 150.
    Hedlund, Martin
    et al.
    n/a.
    Granlund, Gösta H.
    Linköpings universitet, Institutionen för systemteknik, Bildbehandling. Linköpings universitet, Tekniska högskolan.
    Knutsson, Hans
    Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
    A Consistency Operation for Line and Curve Enhancement1982Ingår i: The Computer Society Conference on PR&IP: Anaheim, California, 1982Konferensbidrag (Refereegranskat)
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