mkClusteringInnerFct

resulting object obtained after denoising

fp.res.denois

a numeric indicating the variance of the dataset

fp.var

a numeric value indicating the level of the statistical multitest H0

fp.alpha

'mkClusteringInnerFct' returns the clustering function

internal

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).

mkClusteringInnerFct: mkClusteringInnerFct

Description

Usage

Arguments

Value