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Conference papers
Thin structure filtering framework with non-local means, Gaussian derivatives and spatially-variant mathematical morphology
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.
Cited literature [15 references]
https://hal.univ-reims.fr/hal-01719128
Contributor : Nicolas Passat Connect in order to contact the contributor
Submitted on : Wednesday, February 28, 2018 - 2:11:30 AM
Last modification on : Saturday, January 15, 2022 - 3:56:32 AM
Long-term archiving on: : Monday, May 28, 2018 - 1:59:01 PM
Contributor : Nicolas Passat Connect in order to contact the contributor
Submitted on : Wednesday, February 28, 2018 - 2:11:30 AM
Last modification on : Saturday, January 15, 2022 - 3:56:32 AM
Long-term archiving on: : Monday, May 28, 2018 - 1:59:01 PM
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Nguyen_ICIP_2013.pdf
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