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Publications (10 of 107) Show all publications
Lidayová, K., Gupta, A., Frimmel, H., Sintorn, I.-M., Bengtsson, E. & Smedby, Ö. (2017). Classification of cross-sections for vascular skeleton extraction using convolutional neural networks. In: Medical Image Understanding and Analysis: . Paper presented at MIUA 2017, July 11–13, Edinburgh, UK (pp. 182-194). Springer.
Open this publication in new window or tab >>Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
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2017 (English)In: Medical Image Understanding and Analysis, Springer, 2017, 182-194 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Springer, 2017
Series
Communications in Computer and Information Science, 723
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-318529 (URN)10.1007/978-3-319-60964-5_16 (DOI)978-3-319-60963-8 (ISBN)
Conference
MIUA 2017, July 11–13, Edinburgh, UK
Available from: 2017-06-22 Created: 2017-03-24 Last updated: 2017-06-28Bibliographically approved
Bengtsson, E., Danielsen, H., Treanor, D., Gurcan, M. N., MacAulay, C. & Molnár, B. (2017). Computer-aided diagnostics in digital pathology. Cytometry Part A, 91(6), 551-554.
Open this publication in new window or tab >>Computer-aided diagnostics in digital pathology
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2017 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 6, 551-554 p.Article in journal, Editorial material (Other academic) Published
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-330045 (URN)10.1002/cyto.a.23151 (DOI)000404037600001 ()28640523 (PubMedID)
Available from: 2017-06-22 Created: 2017-09-25 Last updated: 2017-11-25Bibliographically approved
Wieslander, H., Forslid, G., Bengtsson, E., Wählby, C., Hirsch, J.-M., Runow Stark, C. & Kecheril Sadanandan, S. (2017). Deep convolutional neural networks for detecting cellular changes due to malignancy. In: IEEE International Conference on Computer Vision: . Paper presented at ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.. .
Open this publication in new window or tab >>Deep convolutional neural networks for detecting cellular changes due to malignancy
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2017 (English)In: IEEE International Conference on Computer Vision, 2017Conference paper, Poster (with or without abstract) (Refereed)
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-329356 (URN)
Conference
ICCV workshop on Bioimage Computing, Venice, Italy, October 23, 2017.
Funder
EU, European Research Council, 682810Swedish Research Council, 2012-4968
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2017-11-17
Lidayová, K., Frimmel, H., Bengtsson, E. & Smedby, Ö. (2017). Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation. Journal of Medical Imaging, 4, 024004:1-11.
Open this publication in new window or tab >>Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
2017 (English)In: Journal of Medical Imaging, ISSN 2329-4302, E-ISSN 2329-4310, Vol. 4, 024004:1-11 p.Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-318527 (URN)10.1117/1.JMI.4.2.024004 (DOI)000405944600018 ()28466028 (PubMedID)
Available from: 2017-04-28 Created: 2017-03-28 Last updated: 2017-11-17Bibliographically approved
Barrera, T., Hast, A. & Bengtsson, E. (2016). A chronological and mathematical overview of digital circle generation algorithms: Introducing efficient 4- and 8-connected circles. International Journal of Computer Mathematics, 93(8), 1241-1253.
Open this publication in new window or tab >>A chronological and mathematical overview of digital circle generation algorithms: Introducing efficient 4- and 8-connected circles
2016 (English)In: International Journal of Computer Mathematics, ISSN 0020-7160, E-ISSN 1029-0265, Vol. 93, no 8, 1241-1253 p.Article in journal (Refereed) Published
Abstract [en]

Circles are one of the basic drawing primitives for computers and while the naive way of setting up an equation for drawing circles is simple, implementing it in an efficient way using integer arithmetic has resulted in quite a few different algorithms. We present a short chronological overview of the most important publications of such digital circle generation algorithms. Bresenham is often assumed to have invented the first all integer circle algorithm. However, there were other algorithms published before his first official publication, which did not use floating point operations. Furthermore, we present both a 4- and an 8-connected all integer algorithm. Both of them proceed without any multiplication, using just one addition per iteration to compute the decision variable, which makes them more efficient than previously published algorithms.

Keyword
digital circle drawing; all integer arithmetic; connectivity; Bresenham; midpoint and Michener circle
National Category
Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-266119 (URN)10.1080/00207160.2015.1056170 (DOI)000377224200001 ()
Available from: 2015-06-24 Created: 2015-11-05 Last updated: 2017-12-01Bibliographically approved
Ranefall, P., Wählby, C. & Bengtsson, E. (2016). Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. In: : . Paper presented at 4th Nordic Symposium on Digital Pathology. .
Open this publication in new window or tab >>Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections
2016 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Aim

This work describes a proof-of-principle study within the Exchange of Diagnostic Images in Networks (ExDIN) project, for automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. The idea was to mimic the manual grading process: “The assessment is carried out on invasive cancer within the area with the highest number of Ki67-positive cancer cell nuclei/area (hot spot), containing at least 200 cells.”

Method

  • Color deconvolution to separate the image into brown and blue channels.

  • Extract the 10 subsampled tiles (size corresponding to ~200 cells) with the highest values for pre-defined texture and color features.

  • Analyze these tiles in full resolution and compute the maximum positivity (defined as area of positive cells in relation to total cell area, rather than number of cells, since that will speed up the computations and avoid introducing errors due to over- or under segmentation of connected objects).

     

     

     

     

     

     

     

     

     

     

     

     

     

Figure 1. Illustration of the procedure. Hot spot candidates are extracted from low resolution tiles. Then the final hot spot is selected among the corresponding full resolution versions.

The results show good correlation to manual estimates and the procedure takes ~4 minutes/slide.

Future improvements

  • Rules and features defined using machine learning based on training samples given by pathologists.

  • User interface where suggested regions can be deselected manually.

National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-309606 (URN)
Conference
4th Nordic Symposium on Digital Pathology
Projects
ExDIN
Available from: 2016-12-06 Created: 2016-12-06 Last updated: 2016-12-21
Lidayová, K., Frimmel, H., Wang, C., Bengtsson, E. & Smedby, Ö. (2016). Fast vascular skeleton extraction algorithm. Pattern Recognition Letters, 76, 67-75.
Open this publication in new window or tab >>Fast vascular skeleton extraction algorithm
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2016 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 76, 67-75 p.Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-267146 (URN)10.1016/j.patrec.2015.06.024 (DOI)000375135600009 ()
Available from: 2015-07-09 Created: 2015-11-18 Last updated: 2017-12-01Bibliographically approved
García-Olalla, O., Alegre, E., Fernández-Robles, L., Malm, P. & Bengtsson, E. (2015). Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors. Computer Methods and Programs in Biomedicine, 120(1), 49-64.
Open this publication in new window or tab >>Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors
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2015 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 120, no 1, 49-64 p.Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-255045 (URN)10.1016/j.cmpb.2015.03.005 (DOI)000353774600006 ()25887848 (PubMedID)
Available from: 2015-03-23 Created: 2015-06-12 Last updated: 2017-12-04Bibliographically approved
Azar, J., Simonsson, M., Bengtsson, E. & Hast, A. (2015). Automated Classification of Glandular Tissue by Statistical Proximity Sampling. International Journal of Biomedical Imaging, Article ID 943104.
Open this publication in new window or tab >>Automated Classification of Glandular Tissue by Statistical Proximity Sampling
2015 (English)In: International Journal of Biomedical Imaging, ISSN 1687-4188, E-ISSN 1687-4196, 943104Article in journal (Refereed) Published
Abstract [en]

Due to the complexity of biological tissue and variations in staining procedures, features that are based on the explicit extraction of properties from subglandular structures in tissue images may have difficulty generalizing well over an unrestricted set of images and staining variations. We circumvent this problem by an implicit representation that is both robust and highly descriptive, especially when combined with a multiple instance learning approach to image classification. The new feature method is able to describe tissue architecture based on glandular structure. It is based on statistically representing the relative distribution of tissue components around lumen regions, while preserving spatial and quantitative information, as a basis for diagnosing and analyzing different areas within an image. We demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading. The proposed method may be used for glandular classification, also in other tissue types, in addition to general applicability as a region-based feature descriptor in image analysis where the image represents a bag with a certain label (or grade) and the region-based feature vectors represent instances.

National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-230871 (URN)10.1155/2015/943104 (DOI)000362067400001 ()
Available from: 2014-09-01 Created: 2014-09-01 Last updated: 2017-12-05Bibliographically approved
Deepak, R. U., Kumar, R. R., Byju, N. B., Sharathkumar, P. N., Pournami, C., Sibi, S., . . . Sujathan, K. (2015). Computer Assisted Pap Smear Analyser for Cervical Cancer Screening using Quantitative Microscopy. Journal of Cytology & Histology, 6(S3), Article ID 010.
Open this publication in new window or tab >>Computer Assisted Pap Smear Analyser for Cervical Cancer Screening using Quantitative Microscopy
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2015 (English)In: Journal of Cytology & Histology, ISSN 2157-7099, Vol. 6, no S3, 010Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-272446 (URN)10.4172/2157-7099.S3-010 (DOI)
Available from: 2015-04-25 Created: 2016-01-13 Last updated: 2016-01-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1636-3469

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