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Öfverstedt, J., Lindblad, J. & Sladoje, N. (2019). Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information. IEEE Transactions on Image Processing, 28(7), 3584-3597
Open this publication in new window or tab >>Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information
2019 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 28, no 7, p. 3584-3597Article in journal (Refereed) Published
Abstract [en]

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Image registration, set distance, gradient methods, optimization, cost function, iterative algorithms, fuzzy sets, magnetic resonance imaging, transmission electron microscopy
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-377450 (URN)10.1109/TIP.2019.2899947 (DOI)000471067800004 ()
Funder
Vinnova, 2016-02329Swedish Research Council, 2015-05878Swedish Research Council, 2017-04385Vinnova, 2017-02447
Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-07-05Bibliographically approved
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2019). Robust Symmetric Affine Image Registration. In: Swedish Symposium on Image Analysis: . Paper presented at 37th Annual Swedish Symposium on Image Analysis SSBA 2019, Göteborg, Sweden, March 2019.
Open this publication in new window or tab >>Robust Symmetric Affine Image Registration
2019 (English)In: Swedish Symposium on Image Analysis, 2019Conference paper, Published paper (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-379864 (URN)
Conference
37th Annual Swedish Symposium on Image Analysis SSBA 2019, Göteborg, Sweden, March 2019
Available from: 2019-03-21 Created: 2019-03-21 Last updated: 2019-03-28
Bajic, B., Lindblad, J. & Sladoje, N. (2019). Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise. Journal of Electronic Imaging (JEI), 28(1), Article ID 013046.
Open this publication in new window or tab >>Sparsity promoting super-resolution coverage segmentation by linear unmixing in presence of blur and noise
2019 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 28, no 1, article id 013046Article in journal (Refereed) Published
Abstract [en]

We present a segmentation method that estimates the relative coverage of each pixel in a sensed image by each image component. The proposed super-resolution blur-aware model (utilizes a priori knowledge of the image blur) for linear unmixing of image intensities relies on a sparsity promoting approach expressed by two main requirements: (i) minimization of Huberized total variation, providing smooth object boundaries and noise removal, and (ii) minimization of nonedge image fuzziness, responding to an assumption that imaged objects are crisp and that fuzziness is mainly due to the imaging and digitization process. Edge fuzziness due to partial coverage is allowed, enabling subpixel precise feature estimates. The segmentation is formulated as an energy minimization problem and solved by the spectral projected gradient method, utilizing a graduated nonconvexity scheme. Quantitative and qualitative evaluation on synthetic and real multichannel images confirms good performance, particularly relevant when subpixel precision in segmentation and subsequent analysis is a requirement. (C) 2019 SPIE and IS&T

Place, publisher, year, edition, pages
IS&T & SPIE, 2019
Keywords
fuzzy segmentation, super-resolution, deconvolution, linear unmixing, total variation, energy minimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-379780 (URN)10.1117/1.JEI.28.1.013046 (DOI)000460119700046 ()
Funder
Swedish Research Council, 2014-4231Swedish Research Council, 2015-05878Swedish Research Council, 2017-04385
Available from: 2019-03-21 Created: 2019-03-21 Last updated: 2019-03-21Bibliographically approved
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2019). Stochastic Distance Functions with Applications in Object Detection and Image Segmentation. In: Swedish Symposium on Image Analysis: . Paper presented at 37th Annual Swedish Symposium on Image Analysis SSBA 2019, Göteborg, Sweden, March 2019.
Open this publication in new window or tab >>Stochastic Distance Functions with Applications in Object Detection and Image Segmentation
2019 (English)In: Swedish Symposium on Image Analysis, 2019Conference paper, Published paper (Other academic)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-379866 (URN)
Conference
37th Annual Swedish Symposium on Image Analysis SSBA 2019, Göteborg, Sweden, March 2019
Available from: 2019-03-21 Created: 2019-03-21 Last updated: 2019-03-28
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2019). Stochastic Distance Transform. In: Discrete Geometry for Computer Imagery: . Paper presented at 21th International Conference on Discrete Geometry for Computer Imagery (pp. 75-86). Springer
Open this publication in new window or tab >>Stochastic Distance Transform
2019 (English)In: Discrete Geometry for Computer Imagery, Springer, 2019, p. 75-86Conference paper, Published paper (Refereed)
Abstract [en]

The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise. This has a strong negative impact on the accuracy of the DT, which is highly sensitive to spurious noise points. In this study, we consider images represented as discrete random sets and observe statistics of DT computed on such representations. We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise. Both a stochastic Monte Carlo method and a deterministic method for computing the SDT are proposed and compared. Through a series of empirical tests, we demonstrate that the SDT is effective not only in improving the accuracy of the computed distances in the presence of noise, but also in improving the performance of template matching and watershed segmentation of partially overlapping objects, which are examples of typical applications where DTs are utilized.

Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11414
Keywords
distance transform, stochastic, robustness to noise, random sets, monte carlo, template matching, watershed segmentation
National Category
Computer Vision and Robotics (Autonomous Systems) Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-381027 (URN)10.1007/978-3-030-14085-4_7 (DOI)
Conference
21th International Conference on Discrete Geometry for Computer Imagery
Available from: 2019-02-23 Created: 2019-04-03 Last updated: 2019-04-03
Totu, T., Buga, R., Dumitru, A., Costache, M., Sladoje, N. & Stanciu, S. (2018). An Objective Scoring Framework for Histology Slide Image Mosaics Applicable for the Reliable Benchmarking of Image Quality Assessment Algorithms. IEEE Access, 6, 53080-53091
Open this publication in new window or tab >>An Objective Scoring Framework for Histology Slide Image Mosaics Applicable for the Reliable Benchmarking of Image Quality Assessment Algorithms
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2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 53080-53091Article in journal (Refereed) Published
Abstract [en]

The conversion of histology slides into electronic format represents a key element in modern histopathology workflows. The most common way of converting physical histology slides into digital versions consists of tile-based scanning. In such approaches, the entire image of the slide is generated by consecutively scanning adjacent sample regions with a degree of overlap and then stitching these together to constitute an image mosaic. To achieve a high-quality result, the image acquisition protocol for collecting the mosaic tiles requires a recalibration of the microscope when moving from one sample region to another. This recalibration procedure typically involves focus and illumination adjustments, aimed at rendering a homogeneous image mosaic in terms of brightness, contrast, and other important image properties. The accurate evaluation of the digital slide's quality factor is, therefore, an important matter, as it can lead to designing efficient (and automated) mosaic generation protocols. We introduce here a new methodology for the evaluation of image mosaics collected with brightfield microscopy on histology slides, coined Objective Quantifiable Scoring System (OQSS). It relies on objective scoring criteria that take into consideration fundamental characteristics of image mosaics, and on histology specific aspects. We present the theoretical principles of this methodology and discuss the potential utility of this framework as a quality ground-truth tagging mechanism of histology slide image mosaics applicable for the reliable benchmarking of image quality assessment algorithms.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Microscopy, Benchmark testing, Image quality, Pathology, Reliability, Protocols, Lighting
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367991 (URN)10.1109/ACCESS.2018.2868127 (DOI)000447722200001 ()
Funder
VINNOVA
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2018-12-10Bibliographically approved
Bajic, B., Suveer, A., Gupta, A., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement. In: Proc. 15th International Symposium on Biomedical Imaging: . Paper presented at ISBI 2018, April 4–7, Washington, DC (pp. 921-925). IEEE
Open this publication in new window or tab >>Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement
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2018 (English)In: Proc. 15th International Symposium on Biomedical Imaging, IEEE, 2018, p. 921-925Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2018
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367040 (URN)10.1109/ISBI.2018.8363721 (DOI)000455045600210 ()978-1-5386-3636-7 (ISBN)
Conference
ISBI 2018, April 4–7, Washington, DC
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-04-17Bibliographically approved
Gupta, A., Suveer, A., Bajic, B., Pepic, I., Lindblad, J., Sladoje, N. & Sintorn, I.-M. (2018). Denoising of Short Exposure Transmission Electron Microscopy Images using CNN. In: Swedish Symposium on Image Analysis: . Paper presented at SSBA2018, Stockholm, Sweden, March 2018.
Open this publication in new window or tab >>Denoising of Short Exposure Transmission Electron Microscopy Images using CNN
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2018 (English)In: Swedish Symposium on Image Analysis, 2018Conference paper, Published paper (Other academic)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367996 (URN)
Conference
SSBA2018, Stockholm, Sweden, March 2018
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2019-03-06Bibliographically approved
Öfverstedt, J., Sladoje, N. & Lindblad, J. (2018). Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection. In: Swedish Symposium on Image Analysis: . Paper presented at 37th Annual Swedish Symposium on Image Analysis SSBA 2018, Stockholm, Sweden, March 2018.
Open this publication in new window or tab >>Distance Between Vector-valued Images based on Intersection Decomposition with Applications in Object Detection
2018 (English)In: Swedish Symposium on Image Analysis, 2018Conference paper, Published paper (Other academic)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367997 (URN)
Conference
37th Annual Swedish Symposium on Image Analysis SSBA 2018, Stockholm, Sweden, March 2018
Available from: 2018-12-02 Created: 2018-12-02 Last updated: 2019-03-07Bibliographically approved
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2018). Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information.
Open this publication in new window or tab >>Fast and Robust Symmetric Image Registration Based on Intensity and Spatial Information
2018 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367969 (URN)
Funder
VINNOVA, 2016-02329
Available from: 2018-12-01 Created: 2018-12-01 Last updated: 2019-02-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6041-6310

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