Using the Ci_up as the threshold, the diagnostic proportions ( >= Ci_up ) were replaced with 1 and the nondiagnostic proportions( < Ci_up ) were replaced with 0, and then I got a binary diagnosticPlane, and the antidiagnostiPlane were obtained by replacing the diagnostic proportions with 0, and nondiagnostic proportions with 1.

extract_data.m

DEBUG = 1;
namePat = 'gender_backup-%d.mat';

numSubjects = 16;
nLevels = 5;

Ci_up = [0.6914 0.6855 0.6728 0.6404 0.6228]; % upper confidence internal for 5 scale


allProportion = zeros(256,256,5,numSubjects);
MeanProportion = zeros(256,256,5);
allDiagnostic = zeros(256,256,5);
allAntiDiagnostic = zeros(256,256,5);

tic
% compute the proportionPlane for each subject
for i = 1:numSubjects
    if(DEBUG),display(sprintf('Extracting subject %d...\n', i));end
    nameData = sprintf(namePat, i);
    load(nameData, 'correctPlane','totalPlane');
    for j = 1:nLevels
        allProportion(:,:,j,i) = correctPlane(:,:,j) ./ totalPlane(:,:,j);
    end
%     break;
end

% compute the mean of proportionPlane for 16 subjects
for i = 1:nLevels
    for j = 1:numSubjects
        MeanProportion(:,:,i) = MeanProportion(:,:,i) + allProportion(:,:,i,j);
    end
    MeanProportion(:,:,i) = MeanProportion(:,:,i) ./numSubjects;
end

% compute the diagnosticPlane and antiDiagnosticPlane
for i = 1:nLevels
    if(DEBUG),display(sprintf('TEST Level %d...\n', i));
    end
    for x = 1:256
        for y = 1:256
            if( MeanProportion(x,y,i) >= Ci_Up(1,i))
                allDiagnostic(x,y,i) = 1;
                allAntiDiagnostic(x,y,i) = 0;
            else
                allDiagnostic(x,y,i) = 0;
                allAntiDiagnostic(x,y,i) = 1;
            end
        end
    end
end
toc
clear correctPlane totalPlane;

save('allResults.mat', 'allProportion', 'MeanProportion','allDiagnostic', 'allAntiDiagnostic');
clear all;