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  • 1.
    Avenel, Christophe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Carlbom, Ingrid
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Blur detection and visualization in histological whole slide images2015In: Proc. 10th International Conference on Mass Data Analysis of Images and Signals, Leipzig, Germany: IBaI , 2015Conference paper (Refereed)
    Abstract [en]

    Digital pathology holds the promise of improved workflow and also of the use of image analysis to extract features from tissue samples for quantitative analysis to improve current subjective analysis of, for example, cancer tissue. But this requires fast and reliable image digitization. In this paper we address image blurriness, which is a particular problem with very large images or tissue micro arrays scanned with whole slide scanners, since autofocus methods may fail when there is a large variation in image content. We introduce a method to detect, quantify and dis-play blurriness from whole slide images (WSI) in real-time. We describe a blurriness measurement based on an ideal high pass filter in the frequency domain. In contrast with other method our method does not require any prior knowledge of the image content, and it produces a continuous blurriness map over the entire WSI. This map can be displayed as an overlay of the original data and viewed at different levels of magnification with zoom and pan features. The computation time for an entire WSI is around 5 minutes on an average workstation, which is about 180 times faster than existing methods.

  • 2.
    Avenel, Christophe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Fortin, Pierre
    Gouicem, Mourad
    Zaidi, Samia
    Solving the Table Maker's Dilemma on current SIMD architectures2016In: Scalable Computing: Practice and Experience, ISSN 1895-1767, E-ISSN 1895-1767, Vol. 17, no 3, p. 237-250Article in journal (Refereed)
  • 3.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Pacureanu, Alexandra
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Carpenter, Anne E.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    The Giga-pixel Challenge: Full Resolution Image Analysis – Without Losing the Big Picture: An open-source approach for multi-scale analysis and visualization of slide-scanner data2014Conference paper (Other academic)
    Abstract [en]

    The amount of image data in a slide scanner image is usually in the giga-pixel range, inducing challenges for image analysis and visualization. At the same time, in order to maximize the information gained in these experi-ments and integrate expert knowledge with quantitative measurements, it is crucial to maintain the connection be-tween high-resolution per-cell and per-region metrics with lower resolution relational metrics. We present a free and open-source framework for full resolution image analysis of large images, e.g. slide scanner data, with the possibility of visual examination and interaction at multiple resolutions. The interface enables seamless zooming and panning, with the option to toggle multiple layers, such as segmentation masks and classification results, on or off. We make use of the strength and flexibility of existing state-of-the-art open-source software for visualization, creating resolution pyra-mids, image registration and image analysis.

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