Replace LOCATE_testing_highdim.m

LOCATE_testing_highdim.m

@@ -25,14 +25,19 @@ if nargin > 0
 end
 
 % Assigning the Root directories
-root_data_directory = training_image_directory_name;
-results_directory = sprintf('%s/LOCATE_LOO_results_directory',training_image_directory_name);
+root_data_directory = test_image_directory_name;
+results_directory = sprintf('%s/LOCATE_results_directory',test_image_directory_name);
 
-xdir = dir(sprintf('%s/*_BIANCA_LPM.nii.gz',root_data_directory));
+% Creating the results directory
+if ~exist(results_directory)
+    mkdir(results_directory)
+end
 
+xdir = dir(sprintf('%s/*_BIANCA_LPM.nii.gz',train_image_directory_name));
 if numel(xdir) == 0
-    error('Cannot find any input image. Please check your training_image_directoy_name');
+    error('Cannot find any input image. Please check your training_image_directoy_name ');
 else
+    xdir = dir(sprintf('%s/*_BIANCA_LPM.nii.gz',root_data_directory));
     xsplit = regexp(xdir(1).name,'_BIANCA_LPM','split');
     xfeats = dir(sprintf('%s/%s_feature_*',root_data_directory,xsplit{1}));
     if numel(xfeats) == 0
@@ -43,9 +48,9 @@ end
 numfeats = numel(xfeats);
 
 feature_selection_cols = ones(numfeats+2,1);
-if nargin > 1
+if nargin > 2
     if numel(varargin{2}) == 1
-        error('Second input (feature_select) must be a vector with number of elements equal to the number of features specified.');
+        error('Third input (feature_select) must be a vector with number of elements equal to the number of features specified.');
     elseif numel(varargin{2}) < numfeats + 2
         error('Number of columns in feature_select does not match the number of features specified.');
     else
@@ -54,27 +59,33 @@ if nargin > 1
 end
 
 verbose = 0;
-if nargin > 2
+if nargin > 3
     verbose = varargin{3};
 end
 
 if verbose
     feature_selection_cols
-    training_image_directory_name
+    train_image_directory_name
 end
 
-% Creating the results directory
-if ~exist(results_directory)
-    mkdir(results_directory)
+% Loading the Trained Regression model
+try
+    load(sprintf('%s/LOCATE_training_files/RF_regression_model_LOCATE.mat',train_image_directory_name));
+catch
+    error('Training model not found. Run LOCATE_training first or check the training images directory');
 end
 
+if sum(feature_selection_cols) ~= (size(RFmodel.X,2)/20)
+    error('The number of features selected does not match the features used to train the model. Kindly check your feature_select input or use a different model.');
+end
+
+xdir = dir(sprintf('%s/*_BIANCA_LPM.nii.gz',root_data_directory));
 imgfeatmats = cell(numel(xdir),1);
 ventdistfeatmats = cell(numel(xdir),1);
 lesvolfeatmats = cell(numel(xdir),1);
 index_indices_list = cell(numel(xdir),1);
 index_maps = cell(numel(xdir),1);
 
-
 for subj = 1:numel(xdir)    
     
     if verbose
@@ -205,60 +216,23 @@ for testsubj = 1:numel(xdir)
     xsplit = regexp(xdir(testsubj).name,'_BIANCA_LPM','split');
     lesionmaskfile = sprintf('%s/%s_BIANCA_LPM.nii.gz',root_data_directory,xsplit{1}); 
     lesionmask = read_avw(lesionmaskfile);
-    load(sprintf('%s/LOCATE_features_%s.mat',results_directory,xsplit{1}))
-    
-    % Defining the indicies of trainign subjects as everything except the
-    % current test subject
-    trainsubjects = setdiff(1:numel(xdir),testsubj);
-    fprintf('trainsubjects identified! \n');
-    
-    % Concatenating the training features    
-    trainflairfeatmat = [];
-    trainventdistfeatmat = [];
-    trainlesvolfeatmat = [];
-    trainminbestthrs = [];
-    trainmaxbestthrs = [];
-    trainmeanbestthrs = [];
-    for trainsubj = 1:numel(trainsubjects)
-        trainflairfeatmat = [trainflairfeatmat;imgfeatmats{trainsubjects(trainsubj)}];
-        trainventdistfeatmat = [trainventdistfeatmat;ventdistfeatmats{trainsubjects(trainsubj)}];
-        trainlesvolfeatmat = [trainlesvolfeatmat;lesvolfeatmats{trainsubjects(trainsubj)}];
-        trainminbestthrs = [trainminbestthrs;minbestthrs{trainsubjects(trainsubj)}];
-        trainmaxbestthrs = [trainmaxbestthrs;maxbestthrs{trainsubjects(trainsubj)}];
-        trainmeanbestthrs = [trainmeanbestthrs;meanbestthrs{trainsubjects(trainsubj)}]; 
-    end
-    voronoi_train_features_all = [trainventdistfeatmat,trainlesvolfeatmat,trainflairfeatmat];
-    if verbose
-        fprintf('Voronoi training features has been collected! \n');
-    end
-    threshold_array = 0:0.05:0.95;
-    feature_selection_cols_exp = repmat(feature_selection_cols, [numel(threshold_array),1]);
-    feature_selection_cols_exp = feature_selection_cols_exp(:)';
-    voronoi_train_features = voronoi_train_features_all(:,feature_selection_cols_exp>0);
-
-    % Training RF regression model using all the feataures except the test
-    % subject
-    RFmodel_LOO = TreeBagger(1000,voronoi_train_features,trainmaxbestthrs,'Method','Regression',...
-        'numPredictorsToSample','all');
-    if verbose
-        fprintf('Training done! \n');
-    end
     
     % Extract the corresponding test features and index masks from cell
     % arrays
     testflairfeatmat = imgfeatmats{testsubj};
     testventdistfeatmat = ventdistfeatmats{testsubj};
     testlesvolfeatmat = lesvolfeatmats{testsubj};
-%     index_mask = index_maps{testsubj};
-    index_numbers = setdiff(union(index_mask(:),[]),0);%index_indices_list{testsubj};
+    index_mask = index_maps{testsubj};
+    index_numbers = index_indices_list{testsubj};
     voronoi_test_features_all = [testventdistfeatmat,testlesvolfeatmat,testflairfeatmat];
     if verbose
         fprintf('Voronoi test features has been collected! \n');
     end
+    threshold_array = 0:0.05:0.95;
     feature_selection_cols_exp = repmat(feature_selection_cols, [numel(threshold_array),1]);
     feature_selection_cols_exp = feature_selection_cols_exp(:)';
     voronoi_test_features = voronoi_test_features_all(:,feature_selection_cols_exp>0);
-    testmeanbestthrs = predict(RFmodel_LOO,voronoi_test_features);
+    testmeanbestthrs = predict(RFmodel,voronoi_test_features);
     
     %Assigning the values to the final image
     final_binary_lesionmask = zeros(size(lesionmask));
@@ -272,10 +246,10 @@ for testsubj = 1:numel(xdir)
         threshold_mask = threshold_mask + thresh;
         final_binary_lesionmask = final_binary_lesionmask | binary_voronoi_lesionmask;
     end
-%     lesion_masks{testsubj} = final_binary_lesionmask;
-%     thresholds{testsubj} = testmeanbestthrs;
-%     thresholdmap{testsubj} = threshold_mask;
-%     indexmaps{testsubj} = index_mask;
+    lesion_masks{testsubj} = final_binary_lesionmask;
+    thresholds{testsubj} = testmeanbestthrs;
+    thresholdmap{testsubj} = threshold_mask;
+    indexmaps{testsubj} = index_mask;
     
     % Saving the images
     save_avw(index_mask,sprintf('%s/%s_indexmap.nii.gz',results_directory,xsplit{1}),'f',[1 1 1]);
@@ -286,7 +260,8 @@ for testsubj = 1:numel(xdir)
     copying_image_geometry(lesionmaskfile, sprintf('%s/%s_indexmap.nii.gz',results_directory,xsplit{1}), verbose);
     copying_image_geometry(lesionmaskfile, sprintf('%s/%s_thresholdsmap.nii.gz',results_directory,xsplit{1}), verbose);
     copying_image_geometry(lesionmaskfile, sprintf('%s/%s_BIANCA_LOCATE_binarylesionmap.nii.gz',results_directory,xsplit{1}), verbose);
+    
 end
 % Save all the results as a consolidated single folder
-% save(sprintf('%s/Consolidated_LOCATE_output.mat',results_directory),'lesion_masks','thresholds','indexmaps','thresholdmap');
+save(sprintf('%s/Consolidated_LOCATE_output.mat',results_directory),'lesion_masks','thresholds','indexmaps','thresholdmap');