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LOCATE_feature_extraction_per_subject.m

+function LOCATE_feature_extraction_per_subject(feature_select, num_of_modalities, features_path, subject_name, varargin)
+
+% Function for extracting features for LOCATE
+%   Copyright - FMRIB, WIN, University of Oxford
+%   Vaanathi Sundaresan - 25/05/2018
+% 
+%   Mandatory inputs:
+%   feature_select (binary integer array) - vector with elements indicating if the feature has to be included or not. Current order is distance from ventricles, lesion volume and other modalities in alphabetical naming order
+%                 (e.g. If FLAIR is the only modality provided and distance from ventricles is not needed then feature_select = [0, 1, 1])
+%   num_of_modalities (integer) - number of input modalities used (e.g.
+%   num_of_modalities = 1, if only FLAIR is used, num_of_modalities = 2 if
+%   FLAIR and T1 are used)
+%   features_path (string) - path to the directory where features need to be stored
+%   subject_name (string) - To save the LOCATE features file
+%
+%   Example funtional calls:
+%   1. LOCATE_testing(feature_select, num_of_modalities, path_to_file1, ..., path_to_fileN );
+%   path_to_files - starting from the paths to modalities, LPM from BIANCA,
+%   biancamask, brainmask, ventricle distancemap (if the first value of
+%   feature_select is 1)
+  
+addpath('MATLAB');
+
+if ~exist(features_path)
+    mkdir(features_path)
+end
+nargin
+if feature_select(1) == 0
+    if nargin < (2+num_of_modalities+3)
+        error('Number of input paths provided is lesser than those required. Please check the input filepaths provided');
+    end
+else
+    if nargin < (2+num_of_modalities+4)
+        error('Number of input paths provided is lesser than those required. Please check the input filepaths provided');
+    end
+end
+
+flairimage = cell(num_of_modalities,1);
+c = 1;
+for subj_feat_no = 1:num_of_modalities
+    flairimage{subj_feat_no} = read_avw(varargin{c});
+    c = c+1;
+end
+
+lesionmask = read_avw(varargin{num_of_modalities+1});   
+biancamask = read_avw(varargin{num_of_modalities+2});
+brainmask = read_avw(varargin{num_of_modalities+3});
+
+if feature_select(1) == 0
+    try 
+        ventdistmap = read_avw(varargin{num_of_modalities+4});
+    catch
+        ventdistmap = zeros(size(lesionmask));
+    end
+else
+    ventdistmap = read_avw(varargin{num_of_modalities+4});
+end
+
+fprintf('All specified feature image modalities loaded \n');
+        
+% Getting image dimensions and determining up/downsampling factor
+dim = size(lesionmask);
+factor = 150./dim;%floor(max(dim)./dim);
+inv_factor = 1./factor;
+
+% Up/downsampling the images
+lesionmask_resized = imresizen(lesionmask,factor);
+biancamask = imresizen(single(biancamask),factor);
+brainmask = imresizen(single(brainmask),factor);
+biancamask = (biancamask>0) & (brainmask>0);  
+
+% Performing Voronoi tessellation on resampled images
+[~, index_mask, index_numbers] = LOCATE_Voronoi_tessellation_resized(lesionmask_resized, biancamask, inv_factor);
+fprintf('Voronoi Tessellation done! \n')
+index_mask = imresizen(index_mask,inv_factor,'nearest');
+index_numbers = setdiff(union(index_mask(:),[]),0);
+numel(index_numbers)
+% Extractng features from Voronoi regions individually 
+[flairintfeats, ventdistfeats, lesvolfeats, index_numbers, index_mask] ...
+    = LOCATE_test_feature_extraction(lesionmask, ventdistmap, flairimage, index_mask, index_numbers);
+        
+fprintf('LOCATE features extracted! \n')        
+    
+flairintfeats = single(flairintfeats);
+ventdistfeats = single(ventdistfeats);
+lesvolfeats = single(lesvolfeats);
+%minbestthr_values = single(minbestthr_values);
+%maxbestthr_values = single(maxbestthr_values);
+%meanbestthr_values = single(meanbestthr_values);
+index_numbers = single(index_numbers);
+index_mask = single(index_mask);
+
+% Saving the features as an intermediate results .mat file
+save(sprintf('%s/LOCATE_features_%s.mat',features_path,subject_name),'flairintfeats',...
+'ventdistfeats','lesvolfeats','index_numbers','index_mask');
+    
+end