The relation between color spaces and compositional data analysis demonstrated with magnetic resonance image processing applications

This paper presents a novel application of compositional data analysis methods in the context of color image processing. A vector decomposition method is proposed to reveal compositional components of any vector with positive components followed by compositional data analysis to demonstrate the relation between color space concepts such as hue and saturation to their compositional counterparts. The proposed methods are applied to a magnetic resonance imaging dataset acquired from a living human brain and a digital color photograph to perform image fusion. Potential future applications in magnetic resonance imaging are mentioned and the benefits/disadvantages of the proposed methods are discussed in terms of color image processing.Comment: 13 pages, 3 figures, short paper, submitted to Austrian Journal of Statistics compositional data analysis special issue, first revision, fix rendering error in fig

LayNii: a software suite for layer-fMRI

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data

Cortical Auditory Atlas

Human cytoarchitectonic auditory cortex surface atla

Mesoscopic living human brain MRI

Mesoscopic quantification of cortical architecture in the living human brai

py_pRF_motion

for getting DOI

Segmentator v1.5.3

New features in version 1.5: Lasso drawing mode erase function is added to GUI. Deriche filter for determining the smoothness of the gradients. Useful for very high resolution data. Non-linear anisotropic diffusion based smoothing filters (Weickert 1998) are added as a new utility accessible through command line: segmentator_filters -h Fixes in the current version: Minor bugs related to gradient magnitude image exports. Updated links after publication of the paper and some readme cleaning. Fixed module not found error in segmentator_filter

Segmentator paper processing scripts

DOI badge is now fixed