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Blur detection and visualization in histological whole slide images
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.ORCID iD: 0000-0002-1835-921X
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.
2015 (English)In: Proc. 10th International Conference on Mass Data Analysis of Images and Signals, Leipzig, Germany: IBaI , 2015Conference paper, Published 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.

Place, publisher, year, edition, pages
Leipzig, Germany: IBaI , 2015.
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-273832OAI: oai:DiVA.org:uu-273832DiVA, id: diva2:895198
Conference
MDA 2015, July 11–14, Hamburg, Germany
Available from: 2016-01-18 Created: 2016-01-18 Last updated: 2016-01-20

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Avenel, ChristopheCarlbom, Ingrid

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