darkTubes regional max

src/MATLAB/+RSF/darkTubes.m

@@ -21,7 +21,7 @@ bw = imdilate(bw,ones(4*thickness));
 imp(~bw) = 0;
 
 imfm = fibermetric(imp);
-% imfm = imfm .* imregionalmax(imfm);
+imfm = imfm .* imregionalmax(imfm);
 4;
 
 

src/MATLAB/clusterExamples/goLR.m

 tic
-ROOT = '/g/leverjs/Schuman_OCT/OCT/qualifiedYes';
+ROOT = '/g/leverjs/Schuman_OCT/OCT/qualified';
+% ROOT = '/g/leverjs/Schuman_OCT/OCT/qualified';
 
-PID = 'P10010';
-% PID = 'P10125'
+% PID = 'P10010';
+PID = 'P10125'
 flist = dir(fullfile(ROOT,['/' PID '*.LEVER']));
 mx = regexp({flist.name},'P(?<PID>\d+)_(?<scanType>.+)_(?<date>\d+-\d+-\d+)_(?<time>\d+-\d+-\d+)_(?<eye>\w\w)_(?<scanID>.+?)_.*.LEVER','names');
 tblMeta = struct2table(vertcat(mx{:}));
@@ -21,6 +22,10 @@ p = ljsStartParallel(96);
 % radii = {[5,5,5]};
 
 radii = {[8]} % dark tubes
+% radii = {[0.5,8,8],[5,5,5],[8]}; % plate,blob, dark tube
+
+radii
+
 imf = RSF.getImf(tblMeta,radii);
 imf = RSF.quantizeImX(imf,[0,100]);
 d = [];

src/MATLAB/ncdVSvfmd/getStats.m

@@ -43,7 +43,7 @@ for i = 1:length(dxx)
         idxn = find(dxx{i}(:,2)==n & dxx{i}(:,3) == n);
         nt.X = [dxx{i}(idxm,1),dxx{i}(idxn,1),dxx{i}(j,1)];
         nt.ncd = (dxx{i}(j,1));        
-        nt.md = min(pData.md(n),pData.md(m));
+        nt.md = pData.md(m);
         nt.dmd = dmd;
         nt.mn = [m,n];                
         nt.ddx = pData.dx(n) - pData.dx(m);
@@ -54,6 +54,7 @@ for i = 1:length(dxx)
         % find sex
         idx = find(tblMD.subjectID == pData.subjectID(m),1,'first');
         nt.sex = tblMD.gender_full(idx);
+        nt.velocity = nt.dmd ./ years(nt.ddx);
         if abs(nt.dmd)>4
              continue
         end

src/MATLAB/ncdVSvfmd/goEmbedCorr.m

-load('/home/ac/git/rsf/src/MATLAB/ncdVSvfmd/results/qualYes_ncdVSvfmd_[[0.5,8,8]].mat')
+% load('/home/ac/git/rsf/src/MATLAB/ncdVSvfmd/results/qualYes_ncdVSvfmd_[[0.5,8,8]].mat')
 r2 = [];
 
 for i = 1:length(dxx)
@@ -54,7 +54,7 @@ dMD = [];
         end
     end
 
-    NDIMS = 3;
+    NDIMS = 4;
     if all(0==triu(d,1),'all') || size(d,1)<NDIMS
         continue
     end
@@ -72,11 +72,16 @@ dMD = [];
     dEmbed = [dEmbed ; dY];
     dNCD = [dNCD ; diag(d)];
     dMD = [dMD;0*diag(d)];
-    if length(find(abs(dMD)>0)) <1
+    
+    if length(find(abs(dMD)>0)) <4
         continue
     end
     %     [r,p] = corr(dMD,dEmbed);
-    [r,p] = corr(dMD,dEmbed,'rows','pairwise');
+    [r,p] = corr(dMD,dEmbed,'rows','complete');
+    if length(find(dMD<0))<3
+        continue
+    end
+    [r,p,length(find(dMD<0))]
     r2 = [r2,r^2];
 
 end
\ No newline at end of file

src/MATLAB/ncdVSvfmd/goKernelCorr.m

@@ -18,13 +18,15 @@ load('../qualify/onh_rnfl_thickness.mat');
 
 % radii = {[0.5,8,8],[5,5,5],[8]}; % plate,blob
 % radii = {[0.5,8,8],[5,5,5],[0]}; % plate,blob
-radii = {[0.5,8,8]}; % plate
-
+% radii = {[0.5,8,8]}; % plate
+% radii = {[5,5,5]}; % plate
+% radii = {[7,4,4]}; % plate
+radii = {[8]};
+radii
 
 [rho2,tblVelo, pData, dxx]  = kernelCorr(radii,tblVFMD,tblMeta,tblRNFL);
 
-
 [rmse1,~] = regnetCorr(tblVelo);median(rmse1)
 
-saveFile = fullfile(SAVE_FOLDER,['qualYes_ncdVSvfmd_' jsonencode(radii) '.mat']);
-save(saveFile)
+% saveFile = fullfile(SAVE_FOLDER,['qualEns_ncdVSvfmd_' jsonencode(radii) '.mat']);
+% save(saveFile)

src/MATLAB/ncdVSvfmd/kernelCorr.m

@@ -41,17 +41,18 @@ end
 p=ljsStartParallel(96);
 
 imf = RSF.getImf(pData,radii);
-
+% note images are quantized below during building worklist for parpool
 [C,ia,ic] = unique(pData.subjectID);
 
-
+% worklist is buckets by patient. each bucket gets pairwise distances
+% computed (below). 
 workList = [];
 for px = 1:length(C)    
     work1 = [];
     idx = find(ic==px);
     
     work1.imf = imf(idx);
-    
+    work1.imf = RSF.quantizeImX(work1.imf); 
     wl = [idx,idx];
     wl = [wl;nchoosek(idx,2)];
     work1.wl = wl;

src/MATLAB/ncdVSvfmd/regnetCorr.m

@@ -22,8 +22,10 @@ for i = 1:length(xTarget)
     trainY = T;
     trainY(i) = [];
 %     mdl = fitrnet(trainX2,trainY2,'Activations','sigmoid','standardize',false,'LayerSizes',[10]);
-    mdl = fitrnet(trainX,trainY,"OptimizeHyperparameters","auto", "HyperparameterOptimizationOptions",...
-        struct("AcquisitionFunctionName","expected-improvement-plus",'verbose',0,'showplots',false));
+%     mdl = fitrnet(trainX,trainY,"OptimizeHyperparameters","auto", "HyperparameterOptimizationOptions",...
+%         struct("AcquisitionFunctionName","expected-improvement-plus",'verbose',0,'showplots',false));
+    mdl = fitrensemble(trainX,trainY,'OptimizeHyperparameters','all',...
+        'HyperparameterOptimizationOptions',struct('Verbose',0,'ShowPlots',false));
     mdlX{i} = mdl;
     pred = mdl.predict(xTarget(i,:));
     err(i) = abs(T(i) - pred);

src/MATLAB/qualify/goQualify.m

 ROOT = '/g/leverjs/Schuman_OCT/OCT/combined';
-target = '/g/leverjs/Schuman_OCT/OCT/qualifiedYes';
+target = '/g/leverjs/Schuman_OCT/OCT/qualified';
 if ~exist(target,'dir')
     mkdir(target);
 end