Thin structure filtering framework with non-local means, Gaussian derivatives and spatially-variant mathematical morphology

Tuan-Anh Nguyen 1 Alice Dufour 2 Olena Tankyevych 1 Amir Nakib 1 Eric Petit 1 Hugues Talbot 3 Nicolas Passat 4
1 LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
2 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
3 LIGM - Laboratoire d'Informatique Gaspard-Monge
4 CRESTIC - Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804
Abstract : Thin structure filtering is an important preprocessing task for the analysis of 2D and 3D bio-medical images in various contexts. We propose a filtering framework that relies on three approaches that are distinct and infrequently used together: linear, non-linear and non-local. This strategy, based on recent progress both in algorithmic/computational and methodological points of view, provides results that benefit from the advantages of each approach, while reducing their respective weaknesses. Its relevance is demonstrated by validations on 2D and 3D images.
Keywords : thin object filtering mathematical morphology Hessian filtering non-local means angiography
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Conference papers
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Tuan-Anh Nguyen, Alice Dufour, Olena Tankyevych, Amir Nakib, Eric Petit, et al.. Thin structure filtering framework with non-local means, Gaussian derivatives and spatially-variant mathematical morphology. International Conference on Image Processing (ICIP), 2013, Melbourne, Australia. pp.1237-1241, ⟨10.1109/ICIP.2013.6738255⟩. ⟨hal-01719128⟩

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