CRAN/E | DynClust

DynClust

Denoising and Clustering for Dynamical Image Sequence (2D or 3D)+t

Installation

About

A two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.

Key Metrics

Version 3.24
R ≥ 2.10
Published 2022-04-11 735 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Yves Rozenholc

yves.rozenholc@u-paris.fr

Authors

Yves Rozenholc

(UR7537, Univ. Paris Cité)

Christophe Pouzat

(IRMA, CNRS UMR 7501)

Tiffany Lieury

(Cerebral Physiology lab, Univ. Paris Descartes)

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

DynClust archive

Depends

R ≥ 2.10
parallel