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Sladoje, Natasa
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Publications (10 of 29) 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
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
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
Bajic, B., Lindblad, J. & Sladoje, N. (2016). Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study. Journal of Electronic Imaging (JEI), 25(4), Article ID 043020.
Open this publication in new window or tab >>Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study
2016 (English)In: Journal of Electronic Imaging (JEI), ISSN 1017-9909, E-ISSN 1560-229X, Vol. 25, no 4, article id 043020Article in journal (Refereed) Published
Abstract [en]

Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson–Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.

Keywords
image restoration, Poisson noise, mixed Poisson–Gaussian noise, variance stabilizing transform, total variation, Huber potential function
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-308093 (URN)10.1117/1.JEI.25.4.043020 (DOI)000387787000033 ()
Funder
Swedish Research CouncilVINNOVA
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-08-24Bibliographically approved
Bajic, B., Lindblad, J. & Sladoje, N. (2016). Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise. In: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA): . Paper presented at The 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland. IEEE
Open this publication in new window or tab >>Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise
2016 (English)In: 2016 SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Single image super-resolution (SR) reconstructionaims to estimate a noise-free and blur-free high resolution imagefrom a single blurred and noisy lower resolution observation.Most existing SR reconstruction methods assume that noise in theimage is white Gaussian. Noise resulting from photon countingdevices, as commonly used in image acquisition, is, however,better modelled with a mixed Poisson-Gaussian distribution. Inthis study we propose a single image SR reconstruction methodbased on energy minimization for images degraded by mixedPoisson-Gaussian noise.We evaluate performance of the proposedmethod on synthetic images, for different levels of blur andnoise, and compare it with recent methods for non-Gaussiannoise. Analysis shows that the appropriate treatment of signaldependentnoise, provided by our proposed method, leads tosignificant improvement in reconstruction performance.

Place, publisher, year, edition, pages
IEEE, 2016
Series
International Conference on Image Processing Theory Tools and Applications, E-ISSN 2154-512X
Keywords
super-resolution, image zooming, signal dependent noise, energy minimization, variance stabilizing transform, total variation.
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing; Computerized Image Analysis
Identifiers
urn:nbn:se:uu:diva-308095 (URN)10.1109/IPTA.2016.7820962 (DOI)000393589800014 ()978-1-4673-8910-5 (ISBN)
Conference
The 6th International Conference on Image Processing Theory, Tools and Applications, IPTA 2016, Oulu, Finland
Funder
VINNOVA
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2018-08-24Bibliographically approved
Lidayová, K., Lindblad, J., Sladoje, N., Frimmel, H., Wang, C. & Smedby, Ö. (2015). Coverage segmentation of 3D thin structures. In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications: . Paper presented at IPTA 2015, November 10–13, Orléans, France (pp. 23-28). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Coverage segmentation of 3D thin structures
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2015 (English)In: Proc. 5th International Conference on Image Processing Theory, Tools and Applications, Piscataway, NJ: IEEE , 2015, p. 23-28Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2015
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-267161 (URN)10.1109/IPTA.2015.7367089 (DOI)000380472700002 ()978-1-4799-8636-1 (ISBN)
Conference
IPTA 2015, November 10–13, Orléans, France
Funder
Swedish Research Council, 621-2014-6153
Available from: 2016-01-12 Created: 2015-11-18 Last updated: 2018-08-24Bibliographically approved
Tanács, A., Lindblad, J., Sladoje, N. & Kato, Z. (2015). Estimation of linear deformations of 2D and 3D fuzzy objects. Pattern Recognition, 48(4), 1391-1403
Open this publication in new window or tab >>Estimation of linear deformations of 2D and 3D fuzzy objects
2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 4, p. 1391-1403Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-235662 (URN)10.1016/j.patcog.2014.10.006 (DOI)000348880300031 ()
Available from: 2014-10-14 Created: 2014-11-06 Last updated: 2018-08-24Bibliographically approved
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