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Andersson, Thord
Publications (9 of 9) Show all publications
Andersson, T., Borga, M. & Dahlqvist Leinhard, O. (2018). Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds. Pattern Recognition Letters, 112, 340-345
Open this publication in new window or tab >>Geodesic registration for interactive atlas-based segmentation using learned multi-scale anatomical manifolds
2018 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 112, p. 340-345Article in journal (Refereed) Published
Abstract [en]

Atlas-based segmentation is often used to segment medical image regions. For intensity-normalized data, the quality of these segmentations is highly dependent on the similarity between the atlas and the target under the used registration method. We propose a geodesic registration method for interactive atlas-based segmentation using empirical multi-scale anatomical manifolds. The method utilizes unlabeled images together with the labeled atlases to learn empirical anatomical manifolds. These manifolds are defined on distinct scales and regions and are used to propagate the labeling information from the atlases to the target along anatomical geodesics. The resulting competing segmentations from the different manifolds are then ranked according to an image-based similarity measure. We used image volumes acquired using magnetic resonance imaging from 36 subjects. The performance of the method was evaluated using a liver segmentation task. The result was then compared to the corresponding performance of direct segmentation using Dice Index statistics. The method shows a significant improvement in liver segmentation performance between the proposed method and direct segmentation. Furthermore, the standard deviation in performance decreased significantly. Using competing complementary manifolds defined over a hierarchy of region of interests gives an additional improvement in segmentation performance compared to the single manifold segmentation.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Atlas-based segmentation, Image registration, Manifold learning, MRI
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-148304 (URN)10.1016/j.patrec.2018.04.037 (DOI)000443950800049 ()
Available from: 2018-06-07 Created: 2018-06-07 Last updated: 2019-06-14Bibliographically approved
Borga, M., Andersson, T. & Dahlqvist Leinhard, O. (2016). Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images. In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR): . Paper presented at International Conference on Pattern Recognition (ICPR) (pp. 3146-3149). IEEE Computer Society
Open this publication in new window or tab >>Semi-Supervised Learning of Anatomical Manifolds for Atlas-Based Segmentation of Medical Images
2016 (English)In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), IEEE Computer Society, 2016, p. 3146-3149Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
IEEE Computer Society, 2016
Keywords
MRI, atlas-based segmentation
National Category
Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-136004 (URN)10.1109/ICPR.2016.7900118 (DOI)000406771303022 ()978-1-5090-4847-2 (ISBN)978-1-5090-4848-9 (ISBN)
Conference
International Conference on Pattern Recognition (ICPR)
Available from: 2017-03-24 Created: 2017-03-24 Last updated: 2019-06-14Bibliographically approved
Andersson, T., Romu, T., Karlsson, A., Norén, B., Forsgren, M., Smedby, Ö., . . . Dahlqvist Leinhard, O. (2015). Consistent intensity inhomogeneity correction in water–fat MRI. Journal of Magnetic Resonance Imaging, 42(2), 468-476
Open this publication in new window or tab >>Consistent intensity inhomogeneity correction in water–fat MRI
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2015 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 42, no 2, p. 468-476Article in journal (Refereed) Published
Abstract [en]

PURPOSE:

To quantitatively and qualitatively evaluate the water-signal performance of the consistent intensity inhomogeneity correction (CIIC) method to correct for intensity inhomogeneities METHODS: Water-fat volumes were acquired using 1.5 Tesla (T) and 3.0T symmetrically sampled 2-point Dixon three-dimensional MRI. Two datasets: (i) 10 muscle tissue regions of interest (ROIs) from 10 subjects acquired with both 1.5T and 3.0T whole-body MRI. (ii) Seven liver tissue ROIs from 36 patients imaged using 1.5T MRI at six time points after Gd-EOB-DTPA injection. The performance of CIIC was evaluated quantitatively by analyzing its impact on the dispersion and bias of the water image ROI intensities, and qualitatively using side-by-side image comparisons.

RESULTS:

CIIC significantly ( P1.5T≤2.3×10-4,P3.0T≤1.0×10-6) decreased the nonphysiological intensity variance while preserving the average intensity levels. The side-by-side comparisons showed improved intensity consistency ( Pint⁡≤10-6) while not introducing artifacts ( Part=0.024) nor changed appearances ( Papp≤10-6).

CONCLUSION:

CIIC improves the spatiotemporal intensity consistency in regions of a homogenous tissue type. J. Magn. Reson. Imaging 2014.

Place, publisher, year, edition, pages
John Wiley & Sons, 2015
Keywords
water–fat imaging;Dixon imaging;inhomogeneity correction;intensity correction;water;fat quantification
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-112129 (URN)10.1002/jmri.24778 (DOI)000358258600026 ()25355066 (PubMedID)
Note

Funding:

Financial support from the Swedish Research Council (VR/M 2007-2884), the Research Council of Southeast Sweden (FORSS 12621), Linkoping University, Lions Research Foundation in Linkoping, Linkoping University Hospital Research Foundations and the County Council of Ostergotland is gratefully acknowledged.

Available from: 2014-11-16 Created: 2014-11-16 Last updated: 2019-06-14
Andersson, T., Läthén, G., Lenz, R. & Borga, M. (2013). Modified Gradient Search for Level Set Based Image Segmentation. IEEE Transactions on Image Processing, 22(2), 621-630
Open this publication in new window or tab >>Modified Gradient Search for Level Set Based Image Segmentation
2013 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 22, no 2, p. 621-630Article in journal (Refereed) Published
Abstract [en]

Level set methods are a popular way to solve the image segmentation problem. The solution contour is found by solving an optimization problem where a cost functional is minimized. Gradient descent methods are often used to solve this optimization problem since they are very easy to implement and applicable to general nonconvex functionals. They are, however, sensitive to local minima and often display slow convergence. Traditionally, cost functionals have been modified to avoid these problems. In this paper, we instead propose using two modified gradient descent methods, one using a momentum term and one based on resilient propagation. These methods are commonly used in the machine learning community. In a series of 2-D/3-D-experiments using real and synthetic data with ground truth, the modifications are shown to reduce the sensitivity for local optima and to increase the convergence rate. The parameter sensitivity is also investigated. The proposed methods are very simple modifications of the basic method, and are directly compatible with any type of level set implementation. Downloadable reference code with examples is available online.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2013
Keywords
Active contours, gradient methods, image segmentation, level set method, machine learning, optimization, variational problems
National Category
Signal Processing Medical Image Processing
Identifiers
urn:nbn:se:liu:diva-87658 (URN)10.1109/TIP.2012.2220148 (DOI)000314717800017 ()23014748 (PubMedID)
Available from: 2013-01-21 Created: 2013-01-21 Last updated: 2017-12-06
Andersson, T., Romu, T., Norén, B., Forsgren, M., Smedby, Ö., Almer, S., . . . Dahlqvist Leinhard, O. (2012). Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA). In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012: . Paper presented at ISMRM 2012, 20th Annual Meeting & Exhibition, 5 - 11 May 2012, Melbourne, Australia.
Open this publication in new window or tab >>Self-calibrated DCE MRI using Multi Scale Adaptive Normalized Averaging (MANA)
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2012 (English)In: Proceedings of the annual meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2012), 2012, 2012Conference paper, Published paper (Other academic)
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-78899 (URN)
Conference
ISMRM 2012, 20th Annual Meeting & Exhibition, 5 - 11 May 2012, Melbourne, Australia
Note

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Available from: 2012-06-24 Created: 2012-06-24 Last updated: 2019-06-14
Andersson, T., Läthén, G., Lenz, R. & Borga, M. (2009). A Fast Optimization Method for Level Set Segmentation. In: A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen (Ed.), Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings. Paper presented at 16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway (pp. 400-409). Springer Berlin/Heidelberg
Open this publication in new window or tab >>A Fast Optimization Method for Level Set Segmentation
2009 (English)In: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, p. 400-409Conference paper, Published paper (Refereed)
Abstract [en]

Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5575
Keywords
Image segmentation - level set method - optimization - gradient descent - Rprop - variational problems - active contours
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-19313 (URN)10.1007/978-3-642-02230-2_41 (DOI)000268661000041 ()978-3-642-02229-6 (ISBN)978-3-642-02230-2 (ISBN)
Conference
16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway
Available from: 2009-07-09 Created: 2009-06-17 Last updated: 2018-01-23Bibliographically approved
Läthén, G., Andersson, T., Lenz, R. & Borga, M. (2009). Level set based segmentation using gradient descent with momentum. In: SSBA 2009, Symposium on image analysis,2009. Halmstad, Sweden: Halmstad University
Open this publication in new window or tab >>Level set based segmentation using gradient descent with momentum
2009 (English)In: SSBA 2009, Symposium on image analysis,2009, Halmstad, Sweden: Halmstad University , 2009Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Halmstad, Sweden: Halmstad University, 2009
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-45370 (URN)82210 (Local ID)82210 (Archive number)82210 (OAI)
Projects
computer-graphics-image-processing/blood-vessel-segmentation
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-08-31
Läthén, G., Andersson, T., Lenz, R. & Borga, M. (2009). Momentum Based Optimization Methods for Level Set Segmentation. In: Xue-Cheng Tai, Knut Mørken, Marius Lysaker and Knut-Andreas Lie (Ed.), Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen (Ed.), Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings. Paper presented at Second International Conference, SSVM 2009, June 1-5, Voss, Norway (pp. 124-136). Berlin: Springer Berlin/Heidelberg
Open this publication in new window or tab >>Momentum Based Optimization Methods for Level Set Segmentation
2009 (English)In: Momentum Based Optimization Methods for Level Set Segmentation: Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009. Proceedings / [ed] Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen, Berlin: Springer Berlin/Heidelberg, 2009, p. 124-136Conference paper, Published paper (Refereed)
Abstract [en]

Segmentation of images is often posed as a variational problem. As such, it is solved by formulating an energy functional depending on a contour and other image derived terms. The solution of the segmentation problem is the contour which extremizes this functional. The standard way of solving this optimization problem is by gradient descent search in the solution space, which typically suffers from many unwanted local optima and poor convergence. Classically, these problems have been circumvented by modifying the energy functional. In contrast, the focus of this paper is on alternative methods for optimization. Inspired by ideas from the machine learning community, we propose segmentation based on gradient descent with momentum. Our results show that typical models hampered by local optima solutions can be further improved by this approach. We illustrate the performance improvements using the level set framework.

Place, publisher, year, edition, pages
Berlin: Springer Berlin/Heidelberg, 2009
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5567
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-21037 (URN)10.1007/978-3-642-02256-2_11 (DOI)000270543900011 ()3-642-02255-3 (ISBN)978-3-642-02255-5 (ISBN)978-3-642-02256-2 (ISBN)
Conference
Second International Conference, SSVM 2009, June 1-5, Voss, Norway
Note

Original Publication: Gunnar Läthén, Thord Andersson, Reiner Lenz and Magnus Borga, Momentum Based Optimization Methods for Level Set Segmentation, 2009, Lecture Notes in Computer Science 5567: Scale Space and Variational Methods in Computer Vision, 124-136. http://dx.doi.org/10.1007/978-3-642-02256-2_11 Copyright: Springer http://www.springerlink.com/

Available from: 2009-09-28 Created: 2009-09-28 Last updated: 2018-02-19Bibliographically approved
Andersson, T. (2000). Learning in a Reactive Robotic Architecture (ed.). (Licentiate dissertation). Linköping, Sweden: Linköping University, Department of Electrical Engineering
Open this publication in new window or tab >>Learning in a Reactive Robotic Architecture
2000 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

In this licenciate thesis, we discuss how to generate actions from percepts within an autonomous robotic system. In particular, we discuss and propose an original reactive architecture suitable for response generation, learning and self-organization.

The architecture uses incremental learning and supports self organization through distributed dynamic model generation and self-contained components. Signals to and from the architecture are represented using the channel representation, which is presented in that context.

The components of the architecture use a novel and flexible implementation of an artificial neural network. The learning rules for this implementation are derived.

A simulator is presented. It has been designed and implemented in order to test and evaluate the proposed architecture.

Results of a series of experiments on the reactive architecture are discussed and accounted for. The experiments have been performed within three different scenarios, using the developed simulator.

The problem of information representation in robotic architectures is illustrated by a problem of anchoring symbols to visual data. This is presented in the context of the WITAS project.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering, 2000. p. 97
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 817
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-53408 (URN)LIU-TEK-LIC-2000:13 (Local ID)91-7219-694-7 (ISBN)LIU-TEK-LIC-2000:13 (Archive number)LIU-TEK-LIC-2000:13 (OAI)
Presentation
(English)
Supervisors
Available from: 2010-01-20 Created: 2010-01-20 Last updated: 2010-04-29
Organisations

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