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Malmberg, Filip
Publications (10 of 56) Show all publications
Sandberg Melin, C., Malmberg, F. & Söderberg, P. G. (2019). A strategy for OCT estimation of the optic nerve head pigment epithelium central limit-inner limit of the retina minimal distance, PIMD-2π. Acta Ophthalmologica, 97(2), 208-213
Open this publication in new window or tab >>A strategy for OCT estimation of the optic nerve head pigment epithelium central limit-inner limit of the retina minimal distance, PIMD-2π
2019 (English)In: Acta Ophthalmologica, ISSN 1755-375X, E-ISSN 1755-3768, Vol. 97, no 2, p. 208-213Article in journal (Refereed) Published
Abstract [en]

Purpose To develop a semi-automatic algorithm for estimation of pigment epithelium central limit-inner limit of the retina minimal distance averaged over 2 pi radians (PIMD-2 pi) and to estimate the precision of the algorithm. Further, the variances in estimates of PIMD-2 pi were to be estimated in a pilot sample of glaucomatous eyes. Methods Three-dimensional cubes of the optic nerve head (ONH) were captured with a commercial SD-OCT device. Raw cube data were exported for semi-automatic segmentation. The inner limit of the retina was automatically detected. Custom software aided the delineation of the ONH pigment epithelium central limit resolved in 500 evenly distributed radii. Sources of variation in PIMD estimates were analysed with an analysis of variance. Results The estimated variance for segmentations and angles was 130 mu m(2) and 1280 mu m(2), respectively. Considering averaging eight segmentations, a 95 % confidence interval for mean PIMD-2 pi was estimated to 212 +/- 10 mu m (df = 7). The coefficient of variation for segmentation was estimated at 0.05. In the glaucomatous eyes, the within-subject variance for captured volumes and for segmentations within volumes was 10 mu m(2) and 50 mu m(2), respectively. Conclusion The developed semi-automatic algorithm enables estimation of PIMD-2 pi in glaucomatous eyes with relevant precision using few segmentations of each captured volume.

National Category
Ophthalmology Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-362723 (URN)10.1111/aos.13908 (DOI)000459637900020 ()30198106 (PubMedID)
Funder
Gun och Bertil Stohnes Stiftelse
Available from: 2018-09-10 Created: 2018-10-09 Last updated: 2019-10-01
Sjöholm, T., Ekström, S., Strand, R., Ahlström, H., Lind, L., Malmberg, F. & Kullberg, J. (2019). A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis. Scientific Reports, 9, Article ID 6158.
Open this publication in new window or tab >>A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 6158Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-382934 (URN)10.1038/s41598-019-42613-z (DOI)000464652400029 ()30992502 (PubMedID)
Available from: 2019-04-16 Created: 2019-05-07 Last updated: 2019-06-14Bibliographically approved
Sjöholm, T., Ekström, S., Strand, R., Ahlström, H., Lind, L., Malmberg, F. & Kullberg, J. (2019). A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis. Scientific Reports, 9, Article ID 6158.
Open this publication in new window or tab >>A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 6158Article in journal (Refereed) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-393367 (URN)10.1038/s41598-019-42613-z (DOI)
Available from: 2019-04-16 Created: 2019-09-20 Last updated: 2019-09-20Bibliographically approved
Ayyalasomayajula, K. R., Wilkinson, T., Malmberg, F. & Brun, A. (2019). CalligraphyNet: Augmenting handwriting generation with quill based stroke width. Paper presented at 26th IEEE International Conference on Image Processing.
Open this publication in new window or tab >>CalligraphyNet: Augmenting handwriting generation with quill based stroke width
2019 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Realistic handwritten document generation garners a lot ofinterest from the document research community for its abilityto generate annotated data. In the current approach we haveused GAN-based stroke width enrichment and style transferbased refinement over generated data which result in realisticlooking handwritten document images. The GAN part of dataaugmentation transfers the stroke variation introduced by awriting instrument onto images rendered from trajectories cre-ated by tracking coordinates along the stylus movement. Thecoordinates from stylus movement are augmented with thelearned stroke width variations during the data augmentationblock. An RNN model is then trained to learn the variationalong the movement of the stylus along with the stroke varia-tions corresponding to an input sequence of characters. Thismodel is then used to generate images of words or sentencesgiven an input character string. A document image thus cre-ated is used as a mask to transfer the style variations of the inkand the parchment. The generated image can capture the colorcontent of the ink and parchment useful for creating annotated data.

National Category
Computer Systems
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-379633 (URN)
Conference
26th IEEE International Conference on Image Processing
Note

Currently under review

Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-04-08
Malmberg, F., Ciesielski, K. C. & Strand, R. (2019). Optimization of max-norm objective functions in image processing and computer vision. In: Discrete Geometry for Computer Imagery: . Paper presented at DGCI 2019, March 26–28, Marne-la-Vallée, France (pp. 206-218). Springer
Open this publication in new window or tab >>Optimization of max-norm objective functions in image processing and computer vision
2019 (English)In: Discrete Geometry for Computer Imagery, Springer, 2019, p. 206-218Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11414
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-393368 (URN)10.1007/978-3-030-14085-4_17 (DOI)978-3-030-14084-7 (ISBN)
Conference
DGCI 2019, March 26–28, Marne-la-Vallée, France
Available from: 2019-02-23 Created: 2019-09-20 Last updated: 2019-09-20Bibliographically approved
Ayyalasomayajula, K. R., Malmberg, F. & Brun, A. (2019). PDNet: Semantic segmentation integrated with a primal-dual network for document binarization. Pattern Recognition Letters, 121, 52-60
Open this publication in new window or tab >>PDNet: Semantic segmentation integrated with a primal-dual network for document binarization
2019 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 121, p. 52-60Article in journal (Refereed) Published
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366933 (URN)10.1016/j.patrec.2018.05.011 (DOI)000459876700008 ()
Funder
Swedish Research Council, 2012-5743Riksbankens Jubileumsfond, NHS14-2068:1
Available from: 2018-05-16 Created: 2018-11-27 Last updated: 2019-04-04Bibliographically approved
Guglielmo, P., Sjöholm, T., Enblad, G., Strand, R., Kullberg, J., Malberg, F. & Ahlström, H. (2018). Imiomics Using Whole-body FDG PET/MR in Staging and Treatment Response Evaluation of Non-Hodgkin Lymphoma Patients Treated With CAR-T Cells. Paper presented at 31st Annual Congress of the European-Association-of-Nuclear-Medicine (EANM), OCT 13-17, 2018, Dusseldorf, GERMANY. European Journal of Nuclear Medicine and Molecular Imaging, 45, S37-S38
Open this publication in new window or tab >>Imiomics Using Whole-body FDG PET/MR in Staging and Treatment Response Evaluation of Non-Hodgkin Lymphoma Patients Treated With CAR-T Cells
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2018 (English)In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 45, p. S37-S38Article in journal, Meeting abstract (Other academic) Published
Place, publisher, year, edition, pages
Springer, 2018
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-372960 (URN)000449266200052 ()
Conference
31st Annual Congress of the European-Association-of-Nuclear-Medicine (EANM), OCT 13-17, 2018, Dusseldorf, GERMANY
Available from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-01-24Bibliographically approved
Blache, L., Nysjö, F., Malmberg, F., Thor, A., Rodriguez-Lorenzo, A. & Nyström, I. (2018). SoftCut:: A Virtual Planning Tool for Soft Tissue Resection on CT Images. In: Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar (Ed.), Medical Image Understanding and Analysis: . Paper presented at 22nd Medical Image Understanding and Analysis (MIUA), Southampton, UK, 2018 (pp. 299-310). Cham: Springer, 894
Open this publication in new window or tab >>SoftCut:: A Virtual Planning Tool for Soft Tissue Resection on CT Images
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2018 (English)In: Medical Image Understanding and Analysis / [ed] Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar, Cham: Springer, 2018, Vol. 894, p. 299-310Conference paper, Published paper (Refereed)
Abstract [en]

With the increasing use of three-dimensional (3D) models and Computer Aided Design (CAD) in the medical domain, virtual surgical planning is now frequently used. Most of the current solutions focus on bone surgical operations. However, for head and neck oncologic resection, soft tissue ablation and reconstruction are common operations. In this paper, we propose a method to provide a fast and efficient estimation of shape and dimensions of soft tissue resections. Our approach takes advantage of a simple sketch-based interface which allows the user to paint the contour of the resection on a patient specific 3D model reconstructed from a computed tomography (CT) scan. The volume is then virtually cut and carved following this pattern. From the outline of the resection defined on the skin surface as a closed curve, we can identify which areas of the skin are inside or outside this shape. We then use distance transforms to identify the soft tissue voxels which are closer from the inside of this shape. Thus, we can propagate the shape of the resection inside the soft tissue layers of the volume. We demonstrate the usefulness of the method on patient specific CT data.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Communications in Computer and Information Science
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-364351 (URN)10.1007/978-3-319-95921-4_28 (DOI)978-3-319-95920-7 (ISBN)
Conference
22nd Medical Image Understanding and Analysis (MIUA), Southampton, UK, 2018
Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2019-03-14Bibliographically approved
Malmberg, F. & Strand, R. (2018). When Can lp-norm Objective Functions Be Minimized via Graph Cuts?. In: Barneva R., Brimkov V., Tavares J. (Ed.), Combinatorial Image Analysis: . Paper presented at IWCIA 2018, International Workshop on Combinatorial Image Analysis, Nov 22, 2018 - Nov 24, Porto, Portugal (pp. 112-117). Springer
Open this publication in new window or tab >>When Can lp-norm Objective Functions Be Minimized via Graph Cuts?
2018 (English)In: Combinatorial Image Analysis / [ed] Barneva R., Brimkov V., Tavares J., Springer, 2018, p. 112-117Conference paper, Published paper (Refereed)
Abstract [en]

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is sub-modular. This can be interpreted as minimizing the l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lp-norm of the vector. Unfortunately, the submodularity of an l1-norm objective function does not guarantee the submodularity of the corresponding lp-norm objective function. The contribution of this paper is to provide useful conditions under which an lp-norm objective function is submodular for all p>= 1, thereby identifying a large class of lp-norm objective functions that can be minimized via minimal graph cuts.

Techniques based on minimal graph cuts have become a standard tool for solving combinatorial optimization problems arising in image processing and computer vision applications. These techniques can be used to minimize objective functions written as the sum of a set of unary and pairwise terms, provided that the objective function is submodular. This can be interpreted as minimizing the l1l1-norm of the vector containing all pairwise and unary terms. By raising each term to a power p, the same technique can also be used to minimize the lplp-norm of the vector. Unfortunately, the submodularity of an l1l1-norm objective function does not guarantee the submodularity of the corresponding lplp-norm objective function. The contribution of this paper is to provide useful conditions under which an lplp-norm objective function is submodular for all p≥1p≥1, thereby identifying a large class of lplp-norm objective functions that can be minimized via minimal graph cuts.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Minimal graph cuts, lp -norm, Submodularity
National Category
Discrete Mathematics Computer Sciences
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-366961 (URN)10.1007/978-3-030-05288-1_9 (DOI)978-3-030-05287-4 (ISBN)
Conference
IWCIA 2018, International Workshop on Combinatorial Image Analysis, Nov 22, 2018 - Nov 24, Porto, Portugal
Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2019-03-14Bibliographically approved
Strand, R., Malmberg, F., Johansson, L., Lind, L., Sundbom, M., Ahlström, H. & Kullberg, J. (2017). A concept for holistic whole body MRI data analysis, Imiomics. PLoS ONE, 12(2), Article ID e0169966.
Open this publication in new window or tab >>A concept for holistic whole body MRI data analysis, Imiomics
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2017 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 2, article id e0169966Article in journal (Refereed) Published
Abstract [en]

Purpose: To present and evaluate a whole-body image analysis concept, Imiomics (imaging omics) and an image registration method that enables Imiomics analyses by deforming all image data to a common coordinate system, so that the information in each voxel can be compared between persons or within a person over time and integrated with non-imaging data.

Methods: The presented image registration method utilizes relative elasticity constraints of different tissue obtained from whole-body water-fat MRI. The registration method is evaluated by inverse consistency and Dice coefficients and the Imiomics concept is evaluated by example analyses of importance for metabolic research using non-imaging parameters where we know what to expect. The example analyses include whole body imaging atlas creation, anomaly detection, and cross-sectional and longitudinal analysis.

Results: The image registration method evaluation on 128 subjects shows low inverse consistency errors and high Dice coefficients. Also, the statistical atlas with fat content intensity values shows low standard deviation values, indicating successful deformations to the common coordinate system. The example analyses show expected associations and correlations which agree with explicit measurements, and thereby illustrate the usefulness of the proposed Imiomics concept.

Conclusions: The registration method is well-suited for Imiomics analyses, which enable analyses of relationships to non-imaging data, e.g. clinical data, in new types of holistic targeted and untargeted big-data analysis.

National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-316830 (URN)10.1371/journal.pone.0169966 (DOI)000395934400002 ()28241015 (PubMedID)
Available from: 2017-02-27 Created: 2017-03-07 Last updated: 2017-11-29Bibliographically approved
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