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GUI for Multivariate Image Analysis of 4-dimensional data

version 1.12.0.0 (597 KB) by Kateryna Artyushkova
Multivariate Image Analysis of 4-dimensional image sequences using 2-step two-way and three-way ...

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Updated 23 Dec 2010

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This GUI includes is a set of multivariate image analysis methods for analyzing image data sets acquired at two variables. For example: emission excitation image data, spectral or dynamic (temporal) sequences of images acquired at different depths using microscopy.

Two approaches are included:
1. 2-step two-way MIA using PCA, MCR. MAF and Simplisma. In this method, image sequences as a function of variable1 at fixed variable2 are analyzed by two-way method during the 1st step and then the resulted score images at each variable 2 are combined into a new data set and are analyzed by the same two-way method at the 2nd step.

2. Three-way analysis using Parafac, Tucker,three-way augmented MCR and MAF methods. Nonnegativity constraints are imposed during all three model fitting. All three methods will result in score image and associated loadings as a function of both variables.

Classification is also added.

Please refer to a User Guide for more details.
Test image data set is included.

Memory extensive! Might need to spend some time to get your images into the GUI ? resize, subset or bin for it to work.

Requirements: Image Processing toolbox, PLS_toolbox.

GUI USES LARGE NUMBER OF SUBSET FUNCTIONS. I WAS TRYING NOT TO FORGET TO INCLUDE ALL OF THEM. BUT IF YOU GET AN ERROR MISSING ANY OF THEM, PLEASE E-MAIL ME DIRECTLY.

Cite As

Kateryna Artyushkova (2021). GUI for Multivariate Image Analysis of 4-dimensional data (https://www.mathworks.com/matlabcentral/fileexchange/9310-gui-for-multivariate-image-analysis-of-4-dimensional-data), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R14SP3
Compatible with any release
Platform Compatibility
Windows macOS Linux

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