Lα Riemannian weighted centers of mass applied to compose an image filter to diffusion tensor imaging

Charlan Dellon da Silva Alves, Paulo Roberto Oliveira and Ronaldo Malheiros Gregório

Applied Mathematics and Computation, 2021, vol. 390, issue C

Abstract: This paper presents an edge preserving and tensor filtering method for diffusion tensor image. The main idea consists in using the Lα Riemannian centers of mass attached to the edge information estimated in the domain of the diffusion tensor so that the image edges not been smoothed in the filtering process. For α ∈ [1, 2], the method encompasses both the standard case of the Riemannian weighted mean filter (α=2) and the Riemannian weighted median filter (α=1) in only one filter. Aiming to establish the fundamentals for the well-posedness of the proposed filter, called adaptive Riemannian filter (ARF), we claimed a theoretical result previously stated in the literature on the continuity of the Lα Riemannian centers of mass, with respect to the parameter α and the points in the neighborhood of the filtered tensor.

Keywords: Centers of mass; Image processing; Riemannian weighted averages; Riemannian weighted median; Diffusion tensor imaging (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300320305580
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:390:y:2021:i:c:s0096300320305580

DOI: 10.1016/j.amc.2020.125603

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().