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Seeded Segmentation Based on Object Homogeneity
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, 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. 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 Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Radiology.
2012 (English)In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR), 2012, 21-24 p.Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Seeded segmentation methods attempt to solve the segmentation problem in the presence of prior knowledge in the form of a partial segmentation, where a small subset of the image elements (seed-points) have been assigned correct segmentation labels. Common for most of the leading methods in this area is that they seek to find a segmentation where the boundaries of the segmented regions coincide with sharp edges in the image. Here, we instead propose a method for seeded segmentation that seeks to divide the image into areas of homogeneous pixel values. The method is based on the computation of minimal cost paths in a discrete representation of the image, using a novel path-cost function. The utility of the proposed method is demonstrated in a case study on segmentation of white matter hyperintensitities in MR images of the human brain.

Place, publisher, year, edition, pages
2012. 21-24 p.
National Category
Discrete Mathematics Medical Image Processing
Research subject
Computerized Image Analysis; Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-188438ISBN: 978-1-4673-2216-4 (print)OAI: oai:DiVA.org:uu-188438DiVA: diva2:577856
Conference
ICPR 2012, 21 st International Conference on Pattern Recognition, November 11-15 2012, Tsukuba International Congress Center, Tsukuba Science City, Japan
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2013-07-23Bibliographically approved

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http://www.icpr2012.org/http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460062

Authority records BETA

Malmberg, FilipStrand, RobinNordenskjöld, RichardKullberg, Joel

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Computerized Image Analysis and Human-Computer InteractionDivision of Visual Information and InteractionRadiology
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