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Curvilinear structure analysis by ranking the orientation responses of path operators

Abstract : The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure’s orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8% more true positive and 50% less false positives than the next best method, on synthetic and real 3D images.
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Contributor : Odyssée Merveille Connect in order to contact the contributor
Submitted on : Tuesday, February 21, 2017 - 1:53:29 PM
Last modification on : Tuesday, October 19, 2021 - 11:26:22 AM
Long-term archiving on: : Monday, May 22, 2017 - 3:20:18 PM


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Odyssée Merveille, Hugues Talbot, Laurent Najman, Nicolas Passat. Curvilinear structure analysis by ranking the orientation responses of path operators. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2018, 40 (2), pp.304-317. ⟨10.1109/TPAMI.2017.2672972⟩. ⟨hal-01262728v2⟩



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