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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
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 1941-0042Article in journal (Refereed) Epub ahead of print
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)
Available from: 2019-02-20 Created: 2019-02-20 Last updated: 2019-03-03
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: 2019-02-21Bibliographically 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
Öfverstedt, J., Lindblad, J. & Sladoje, N. (2018). Stochastic Distance Transform.
Open this publication in new window or tab >>Stochastic Distance Transform
2018 (English)Manuscript (preprint) (Other academic)
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.

National Category
Computer Vision and Robotics (Autonomous Systems) Discrete Mathematics
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-367970 (URN)
Available from: 2018-12-01 Created: 2018-12-01 Last updated: 2019-02-21Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps. In: Swedish Symposium on Deep Learning: . Paper presented at 2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden.
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomeruli detection by fusion of CNN and LBP maps
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2018 (English)In: Swedish Symposium on Deep Learning, 2018Conference paper, Oral presentation with published abstract (Other academic)
Keywords
Machine learning
National Category
Medical Image Processing Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368016 (URN)
Conference
2nd Swedish Symposium on Deep Learning, 5-6 September, 2018,Göteborg, Sweden
Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-03-14Bibliographically approved
Wetzer, E., Lindblad, J., Sintorn, I.-M., Hultenby, K. & Sladoje, N. (2018). Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps. In: Workshop on BioImage Computing @ ECCV 2018: . Paper presented at European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany. Springer
Open this publication in new window or tab >>Towards automated multiscale imaging and analysis in TEM: Glomerulus detection by fusion of CNN and LBP maps
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2018 (English)In: Workshop on BioImage Computing @ ECCV 2018, Springer, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Glomerulal structures in kidney tissue have to be analysed at a nanometer scale for several medical diagnoses. They are therefore commonly imaged using Transmission Electron Microscopy. The high resolution produces large amounts of data and requires long acquisition time, which makes automated imaging and glomerulus detection a desired option. This paper presents a deep learning approach for Glomerulus detection, using two architectures, VGG16 (with batch normalization) and ResNet50. To enhance the performance over training based only on intensity images, multiple approaches to fuse the input with texture information encoded in local binary patterns of different scales have been evaluated. The results show a consistent improvement in Glomerulus detection when fusing texture-based trained networks with intensity-based ones at a late classification stage.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Texture Analysis, Convolutional Neural Networks, Machine learning
National Category
Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-368015 (URN)
Conference
European Conference on Computer Vision - ECCV 2018, 8-14 September, Munich, Germany
Note

Paper in print

Available from: 2018-12-03 Created: 2018-12-03 Last updated: 2019-03-14Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2017). Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction. In: Swedish Symposium on Image Analysis: . Swedish Society for Automated Image Analysis
Open this publication in new window or tab >>Cilia ultrastructural visibility enhancement by multiple instance registration and super-resolution reconstruction
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2017 (English)In: Swedish Symposium on Image Analysis, Swedish Society for Automated Image Analysis , 2017Conference paper, Published paper (Other academic)
Place, publisher, year, edition, pages
Swedish Society for Automated Image Analysis, 2017
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-335371 (URN)
Available from: 2017-12-04 Created: 2017-12-04 Last updated: 2018-08-24
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6041-6310

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