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Fuzzy along spatial relation in 3D. Application to anatomical structures in maxillofacial CBCT

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Date
2015
Notes
Lecture Notes in Computer Science, Vol. 9279
Dewey
Principes généraux des mathématiques
Sujet
Machine learning; medical imaging; Spatial relations; Fuzzy reasoning; Dental imaging
DOI
http://dx.doi.org/10.1007/978-3-319-23231-7_25
Conference name
18th International Conference on Image Analysis and Processing (ICIAP)
Conference date
09-2015
Conference city
Genoa
Conference country
Italy
Book title
Image Analysis and Processing — ICIAP 2015: 18th International Conference
Author
Murino, Vittorio; Puppo, Enrico
Publisher
Springer
Publisher city
Cham
Year
08-2015
Pages number
721
ISBN
978-3-319-23230-0
Book URL
10.1007/978-3-319-23231-7
URI
https://basepub.dauphine.fr/handle/123456789/16246
Collections
  • LAMSADE : Publications
Metadata
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Author
Evain, Timothée
162010 Laboratoire Traitement et Communication de l'Information [LTCI]
Ripoche, Xavier
Atif, Jamal
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Bloch, Isabelle
162010 Laboratoire Traitement et Communication de l'Information [LTCI]
Type
Communication / Conférence
Item number of pages
271-281
Abstract (EN)
Spatial relations have proved to be of great importance in computer vision and image understanding. One issue is their modeling in the image domain, hence allowing for their integration in segmentation and recognition algorithms. In this paper, we focus on the “along” spatial relation. Based on a previous work in 2D, we propose extensions to 3D. Starting from the inter-objects region, we demonstrate that the elongation of the interface between the objects and this region gives a good evaluation of the alongness degree. We also integrate distance information to take into account only close objects parts. Then we describe how to define the alongness relation within the fuzzy set theory. Our method gives a quantitative satisfaction degree of the relation, reliable for differentiating spatial situations. An original example on the maxillofacial area in Cone-Beam Computed Tomography (CBCT) illustrates how the proposed approach could be used to recognize elongated structures.

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