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Publications (10 of 41) Show all publications
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
Gupta, A., Suveer, A., Lindblad, J., Dragomir, A., Sintorn, I.-M. & Sladoje, N. (2017). Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images. In: Image Analysis: Part I. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 407-418). Springer
Open this publication in new window or tab >>Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images
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2017 (English)In: Image Analysis: Part I, Springer, 2017, p. 407-418Conference paper, Published paper (Refereed)
Abstract
Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10269
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334218 (URN)10.1007/978-3-319-59126-1_34 (DOI)978-3-319-59125-4 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Funder
VINNOVA, 2016-02329
Available from: 2017-05-19 Created: 2017-11-21 Last updated: 2018-08-24Bibliographically approved
Bombrun, M., Ranefall, P., Lindblad, J., Allalou, A., Partel, G., Solorzano, L., . . . Wählby, C. (2017). Decoding gene expression in 2D and 3D. In: Image Analysis: Part II. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 257-268). Springer
Open this publication in new window or tab >>Decoding gene expression in 2D and 3D
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-333686 (URN)10.1007/978-3-319-59129-2_22 (DOI)978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Projects
TissueMaps
Available from: 2017-05-19 Created: 2017-11-16 Last updated: 2018-08-24Bibliographically approved
Öfverstedt, J., Sladoje, N. & Lindblad, J. (2017). Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection. In: Mathematical Morphology and its Applications to Signal and Image Processing: . Paper presented at ISMM 2017, May 15–17, Fontainebleau, France (pp. 395-407). Springer, 10225
Open this publication in new window or tab >>Distance between vector-valued fuzzy sets based on intersection decomposition with applications in object detection
2017 (English)In: Mathematical Morphology and its Applications to Signal and Image Processing, Springer, 2017, Vol. 10225, p. 395-407Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel approach to measuring distance between multi-channel images, suitably represented by vector-valued fuzzy sets. We first apply the intersection decomposition transformation, based on fuzzy set operations, to vector-valued fuzzy representations to enable preservation of joint multi-channel properties represented in each pixel of the original image. Distance between two vector-valued fuzzy sets is then expressed as a (weighted) sum of distances between scalar-valued fuzzy components of the transformation. Applications to object detection and classification on multi-channel images and heterogeneous object representations are discussed and evaluated subject to several important performance metrics. It is confirmed that the proposed approach outperforms several alternative single-and multi-channel distance measures between information-rich image/ object representations.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10225
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334377 (URN)10.1007/978-3-319-57240-6_32 (DOI)978-3-319-57239-0 (ISBN)
Conference
ISMM 2017, May 15–17, Fontainebleau, France
Available from: 2017-04-12 Created: 2017-11-22 Last updated: 2018-08-24Bibliographically approved
Sladoje, N. & Lindblad, J. (2017). Distance Between Vector-Valued Representations of Objects in Images with Application in Object Detection and Classification. In: Brimkov, Valentin E. & Barneva, Reneta P. (Ed.), In Proc. of the 18th International Workshop on Combinatorial Image Analysis, IWCIA2017: . Paper presented at 18th International Workshop on Combinatorial Image Analysis, IWCIA 2017, June 19-21, 2017, Plovdiv, Bulgaria. (pp. 243-255). Springer, 10256
Open this publication in new window or tab >>Distance Between Vector-Valued Representations of Objects in Images with Application in Object Detection and Classification
2017 (English)In: In Proc. of the 18th International Workshop on Combinatorial Image Analysis, IWCIA2017 / [ed] Brimkov, Valentin E. & Barneva, Reneta P., Springer, 2017, Vol. 10256, p. 243-255Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel approach to measuring distances between objects in images, suitable for information-rich object representations which simultaneously capture several properties in each image pixel. Multiple spatial fuzzy sets on the image domain, unified in a vector-valued fuzzy set, are used to model such representations. Distance between such sets is based on a novel point-to-set distance suitable for vector-valued fuzzy representations. The proposed set distance may be applied in, e.g., template matching and object classification, with an advantage that a number of object features are simultaneously considered. The distance measure is of linear time complexity w.r.t. the number of pixels in the image. We evaluate the performance of the proposed measure in template matching in presence of noise, as well as in object detection and classification in low resolution Transmission Electron Microscopy images.

Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10256
Keywords
Membership Function, Object Representation, Template Match, Fuzzy Membership Function, Catchment Basin
National Category
Discrete Mathematics Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334200 (URN)10.1007/978-3-319-59108-7_19 (DOI)000432061200019 ()978-3-319-59107-0 (ISBN)978-3-319-59108-7 (ISBN)
Conference
18th International Workshop on Combinatorial Image Analysis, IWCIA 2017, June 19-21, 2017, Plovdiv, Bulgaria.
Funder
VINNOVA
Available from: 2017-11-21 Created: 2017-11-21 Last updated: 2018-08-24Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2017). Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction. In: Image Analysis: Part II. Paper presented at SCIA 2017, June 12–14, Tromsø, Norway (pp. 362-374). Springer
Open this publication in new window or tab >>Enhancement of cilia sub-structures by multiple instance registration and super-resolution reconstruction
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2017 (English)In: Image Analysis: Part II, Springer, 2017, p. 362-374Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10270
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-334225 (URN)10.1007/978-3-319-59129-2_31 (DOI)978-3-319-59128-5 (ISBN)
Conference
SCIA 2017, June 12–14, Tromsø, Norway
Available from: 2017-05-19 Created: 2017-11-21 Last updated: 2018-08-24Bibliographically approved
Gupta, A., Suveer, A., Lindblad, J., Dragomir, A., Sintorn, I.-M. & Sladoje, N. (2017). False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network. In: Swedish Symposium on Image Analysis: . Paper presented at SWEDISH SYMPOSIUM ON IMAGE ANALYSIS 2017 (SSBA), 13-15 March 2017, Linköping, Sweden. Swedish Society for Automated Image Analysis
Open this publication in new window or tab >>False positive reduction of cilia detected in low resolution TEM images using a convolutional neural network
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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-335454 (URN)
Conference
SWEDISH SYMPOSIUM ON IMAGE ANALYSIS 2017 (SSBA), 13-15 March 2017, Linköping, Sweden
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2018-08-24Bibliographically approved
Suveer, A., Sladoje, N., Lindblad, J., Dragomir, A. & Sintorn, I.-M. (2016). Automated detection of cilia in low magnification transmission electron microscopy images using template matching. In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on: . Paper presented at IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016 (pp. 386-390). IEEE
Open this publication in new window or tab >>Automated detection of cilia in low magnification transmission electron microscopy images using template matching
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2016 (English)In: Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, IEEE, 2016, p. 386-390Conference paper, Published paper (Other academic)
Abstract [en]

Ultrastructural analysis using Transmission Electron Microscopy (TEM) is a common approach for diagnosing primary ciliary dyskinesia. The manually performed diagnostic procedure is time consuming and subjective, and automation of the process is highly desirable. We aim at automating the search for plausible cilia instances in images at low magnification, followed by acquisition of high magnification images of regions with detected cilia for further analysis. This paper presents a template matching based method for automated detection of cilia objects in low magnification TEM images, where object radii do not exceed 10 pixels. We evaluate the performance of a series of synthetic templates generated for this purpose by comparing automated detection with results manually created by an expert pathologist. The best template achieves a detection at equal error rate of 47% which suffices to identify densely populated cilia regions suitable for high magnification imaging.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords
Image resolution, Transmission Electron Microscopy, Object detection, Shape, Image analysis, Template matching
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-308090 (URN)10.1109/ISBI.2016.7493289 (DOI)000386377400093 ()9781479923496 (ISBN)9781479923502 (ISBN)
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-08-24Bibliographically approved
Bajic, B., Lindblad, J. & Sladoje, N. (2016). Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy. In: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI): . Paper presented at IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016 (pp. 123-127). IEEE
Open this publication in new window or tab >>Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy
2016 (English)In: 2016 Ieee 13Th International Symposium On Biomedical Imaging (ISBI), IEEE, 2016, p. 123-127Conference paper, Published paper (Other academic)
Abstract [en]

Noise and blur, present in images after acquisition, negatively affect their further analysis. For image enhancement when the Point Spread Function (PSF) is unknown, blind deblurring is suitable, where both the PSF and the original image are simultaneously reconstructed. In many realistic imaging conditions, noise is modelled as a mixture of Poisson (signal-dependent) and Gaussian (signal independent) noise. In this paper we propose a blind deconvolution method for images degraded by such mixed noise. The method is based on regularized energy minimization. We evaluate its performance on synthetic images, for different blur kernels and different levels of noise, and compare with non-blind restoration. We illustrate the performance of the method on Transmission Electron Microscopy images of cilia, used in clinical practice for diagnosis of a particular type of genetic disorders.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Symposium on Biomedical Imaging, ISSN 1945-7928
Keywords
Image restoration, Minimization, Estimation, Transmission electron microscopy, Noise measurement, PSNR, Total variation
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-308086 (URN)10.1109/ISBI.2016.7493226 (DOI)000386377400030 ()9781479923496 (ISBN)9781479923502 (ISBN)
Conference
IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-08-24Bibliographically approved
Drazic, S., Sladoje, N. & Lindblad, J. (2016). Estimation of Feret's diameter from pixel coverage representation of a shape. Pattern Recognition Letters, 80, 37-45
Open this publication in new window or tab >>Estimation of Feret's diameter from pixel coverage representation of a shape
2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 80, p. 37-45Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-307867 (URN)10.1016/j.patrec.2016.04.021 (DOI)000382312200006 ()
Available from: 2016-05-16 Created: 2016-11-22 Last updated: 2018-08-24Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7312-8222

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