DisTools examples: Brain MRI

Some exercises are defined on the basis of Brain MRI data. It is assumed that readers are familiar with PRTools and will consult the following pages where needed:

This dataset is described in:

A. Ulas, R.P.W. Duin, U. Castellani, M. Loog, M. Bicego, V. Murino, M. Bellani, S. Cerruti, M. Tansella, and P. Brambilla, Dissimilarity-based Detection of Schizophrenia, Proc. ICPR 2010 workshop on Pattern Recognition Challenges in FMRI Neuroimaging, 2010.

It is based 13 dissimilarity measures between 14 ROIs in MRI brain images, resulting in 13×14 = 182 dissimilarity matrices. Here is an initial experiment.

D = brainmri;       % Loads a 13*14 cell array of dissimilarity matrices
E = zeros(size(D)); % Space for errors
F = zeros(size(D)); % Space for values of NEF
for j=1:13, for i=1:14
E(j,i)=nne(D{j,i}); % LOO 1NN errors
F(j,i)=nef(D{j,i}*makesym*pe_em);  % NEF values
end, end

To get an idea of the characteristics of the data we create a scatterplot of the results:

A = prdataset([E(:) F(:)],repmat([1:13]',14,1));
xlabel Error
ylabel NEF
title 'NEF versus 1NN error for all BrainMRI dismats'


The picture shows that the dissimilarity measures 1 and 7 are perhaps Euclidean. This can be verified by the names of the datasets in which the distance measures are reported:



The above experiments are based on the given dissimilarities of individual dissimilarity matrices. Try to improve the results in the follwing ways:

  1. Classifiers in dissimilarity space.
  2. Classifiers in an embedded space.
  3. Classifiers based on a combination of dissimilarity measures: are there specific ROIs informative?
  4. Classifiers based on a combination of ROIs: what are the most inforamtive measures?
  5. Classifiers based on an overal combination of all dissimilarity matrices.

elements: datasets datafiles cells and doubles mappings classifiers mapping types.
operations: datasets datafiles cells and doubles mappings classifiers stacked parallel sequential dyadic.
user commands: datasets representation classifiers evaluation clustering examples support routines.
introductory examples: Introduction Scatterplots Datasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reduction Combining classifiers Dissimilarities.
advanced examples.


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