DynClust-package: non-parametric denoising and clustering method of noisy images both indexed by time and space
Description
Two-stage method for the denoising and clustering of stack of noisy images acquired over time, based on the simple assumption that a finite sequence of noisy images both indexed by time and space is composed of noisy versions of only a limited amount of dynamic features. The aim of the method is first to denoise signals using both the spatial and temporal information contained in the data, and then cluster the denoised signals depending on their dynamic features. Two signals are considered to have similar features if their difference does not significantly deviate from zero. By comparing difference signals, no assumption is therefore made on the shape of the theoretical signals. In order for the method to be applicable to experimental data, the data should be normally distributed (or at least follow a symmetric distribution) with a constant variance. Also the number of observations n must be of the form n=d^2. Moreover, the method is based on the implicit assumption that, for a given data set, almost each dynamic feature is present in two or more pixels. The use of the method can be time-consuming depending on the size of the data-array (see arguments fp.mask.size, and fp.nproc of the callDenoiseVoxel function in order to reduce the computation time).Details
ll{Package: DynClust
Type: Package
Title: non-parametric denoising and clustering method of noisy images both indexed by time and space
Version: 2.2
Date: 2012-11-15
Author: Tiffany Lieury,Christophe Pouzat, Yves Rozenholc
Maintainer: Tiffany Lieury
Depends: R (>= 2.15), parallel
License: GPL (>=2)
}