Biomedical texture operators and aggregation functions : a methodological review and user's guide

Depeursinge, Adrien (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; École Polytechnique Fédérale de Lausanne (EPFL), Switzerland) ; Fageot, Julien (École Polytechnique Fédérale de Lausanne (EPFL), Switzerland)

This chapter reviews most popular texture analysis approaches under novel comparison axes that are specific to biomedical imaging. A concise checklist is proposed as a user guide to assess the relevance of each approach for a particular medical or biological task in hand. We revealed that few approaches are regrouping most of the desirable properties for achieving optimal performance. In particular, moving frames texture representations based on learned steerable operators showed to enable data-specific and rigid-transformation-invariant characterization of local directional patterns, the latter being a fundamental property of biomedical textures. Potential limitations of having recourse to data augmentation and transfer learning for deep convolutional neural networks and dictionary learning approaches to palliate the lack of large annotated training collections in biomedical imaging are mentioned. We conclude by summarizing the strengths and limitations of current approaches, providing insights on key aspects required to build the next generation of biomedical texture analysis approaches.


Keywords:
Faculty:
Economie et Services
School:
HEG-VS
Subject(s):
Informatique
Publisher:
London, Academic Press
Date:
London
Academic Press
2017
Pagination:
pp. 55–94
Published in
Biomedical texture analysis : fundamentals, tools and challenges
DOI:
ISBN:
978-0-12-812133-7
External resources:
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2017-11-11, last modified 2018-12-20

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