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Tensor Voting: Current State, Challenges and New Trends in the Context of Medical Image Analysis
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0002-6827-9162
KTH, School of Technology and Health (STH), Medical Engineering, Medical Image Processing and Visualization.ORCID iD: 0000-0001-5765-2964
2015 (English)In: Visualization and Processing of Higher Order Descriptors for Multi-Valued Data / [ed] Ingrid Hotz and Thomas Schultz, Springer Science+Business Media B.V., 2015, 163-187 p.Chapter in book (Refereed)
Abstract [en]

Perceptual organisation techniques aim at mimicking the human visual system for extracting salient information from noisy images. Tensor voting has been one of the most versatile of those methods, with many different applications both in computer vision and medical image analysis. Its strategy consists in propagating local information encoded through tensors by means of perception-inspired rules. Although it has been used for more than a decade, there are still many unsolved theoretical issues that have made it challenging to apply it to more problems, especially in analysis of medical images.

The main aim of this chapter is to review the current state of the research in tensor voting, to summarise its present challenges, and to describe the new trends that we foresee will drive the research in this field in the next few years. Also, we discuss extensions of tensor voting that could lead to potential performance improvements and that could make it suitable for further medical applications.

Place, publisher, year, edition, pages
Springer Science+Business Media B.V., 2015. 163-187 p.
, Mathematics and Visualization, ISSN 1612-3786
Keyword [en]
Tensor Voting, Medical Image Analysis
National Category
Medical Image Processing
URN: urn:nbn:se:kth:diva-177480DOI: 10.1007/978-3-319-15090-1_9ISI: 000380461100009ScopusID: 2-s2.0-84936994024ISBN: 978-3-319-15089-5OAI: diva2:872960
Swedish Research Council, 2012-3512

QC 20151214

Available from: 2015-11-20 Created: 2015-11-20 Last updated: 2016-08-23Bibliographically approved

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