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count2011Hough.m
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count2011Hough.m
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function metrics = count2011Hough(minRad, maxRad, Sens, pxlSize, p1min,p1max,p2min,p2max,p3min,p3max,minIn,cutoff);
% counts2011Test selects labelled protein aggregate areas in retinal images
%
% user can select min/max ranges for certain morphological parameters to
% facilitate the detection
%
% STEPS:
% 1) run the program my writing its name with the input parameters in
% brackets
% 2) you will be promped to choose an image for analysis from your computer
% 3) segmented figure with selected aggregates areas labeled with metrics
% will be displayed and saved to disk together with a .MAT file containing all
% metrics and a .TXT file with some of the metrics
%
% SYNOPSIS metrics = counts2011Hough(minRad, maxRad, Sens, pxlSize, p1min,p1max,p2min,p2max,p3min,p3max,minIn,cutoff)
%
% INPUT minRad : smallest circle radius
% maxRad : largest circle radius
% Sens : sensitivity of the fit
% p1min : lower boundary of parameter one (area)
% p1max : upper boundary of parameter one (area)
% p2min : lower boundary of parameter two (perimeter)
% p2max : upper boundary of parameter two (perimeter)
% p3min : lower boundary of parameter three (intensity at
% centroid)
% p3max : upper boundary of parameter three (intensity at
% centroid)
% minIn : minimal pixel intensity in any aggregate
% cutoff : automated threshold correction
% pxlSize : number of microns per pixel
%
% OUTPUT metrics : The coordinates and radii of detected circles
%
% DEPENDENCES count2011Hough uses {Gauss2D, cutFirstHistMode}
%
% example run: metrics = count2011Hough;
%
% Alexandre Matov, Januart 6th, 2023
%%
[fileName,dirName] = uigetfile('*.tif','Julie, please select a TIF file for analysis');
aux1 = imread([dirName,filesep,fileName]);
if nargin<2
pxlSize = 0.09; % microns - was 0.08 but found in my notes 0.09
end
if nargin<6
p1min = 500; % min area in pixels (default 500)
p1max = 8000; % max area in pixels (default 8000)
p2min = 120; % min perimeter around the aggregate (default 120)
p2max = 400; % max perimeter around the aggregate (dafault 400)
p3min = 1000; % min perimeter around the aggregate (default 6000)
p3max = 65000; % max perimeter around the aggregate (dafault 20000)
end
if nargin<5
minIn = 3600; % min pixel intensity in aggregates
end
if nargin<6
cutoff = 1.25; % histogram cutoff factor
end
PixelSize = 0.08 ; % microns per pixel
% load images for analysis testing
%aux1 = imread('A:\Amydis\Glaucoma SDEB Eye #2\Bottom\GC 090622-2 Bottom 1 40x 2011 Ab-647 01-Image Export-01\GC 090622-2 Bottom 1 40x 2011 Ab-647 01-Image Export-01_ChS1-T2_ORG.tif');
%aux1 = imread('A:\Amydis\Glaucoma SDEB Eye #2\Bottom\GC 090622-2 Bottom 1 40x 2011 Ab-647 02-Image Export-02\GC 090622-2 Bottom 1 40x 2011 Ab-647 02-Image Export-02_ChS1-T2_ORG.tif');
%aux1 = imread('A:\Amydis\Glaucoma SDEB Eye #2\Bottom\GC 090622-2 Bottom 1 40x 2011 Ab-647 03-Image Export-03\GC 090622-2 Bottom 1 40x 2011 Ab-647 03-Image Export-03_ChS1-T2_ORG.tif');
%aux1 = imread('A:\Amydis\AMYDIS FIH - COHORT 1\2\FF OD\POST DOSE_012.tif');
Igray = Gauss2D(double(aux1),1); % filtering of high frequency background noise
Iblur = Gauss2D(double(aux1),4); % filtering of background nonspecific intensity
Idiff = Igray - Iblur; % difference of gaussians
Idiff(find(Idiff<0))=0; % clipping of negative values
figure, imshow(Igray,[]);
% automated selection of pixels which belong to foreground
[cutoffInd, cutoffV] = cutFirstHistMode(Igray,0);
threshold = cutoffV*cutoff;
%I = rgb2gray(I);
I = Igray>threshold;
%imshow (I);
%figure,imshow(I.*Igray,[]);
%mask = zeros(sw(ize(I));
%mask(25:end-25,25:end-25) = 1;
%imshow(mask)
%title('Initial Contour Location')
%bw = activecontour(Igray,mask,300);
%imshow(bw)
%title('Segmented Image, 300 Iterations')
X = bwlabel(I.*Igray);
stats = regionprops(X,'all'); %
[centers, radii, metric] = imfindcircles(X,[10 50],"Sensitivity",0.95);
% 'ObjectPolarity','bright');
n = 5;
centersStrong5 = centers(1:n,:);
radiiStrong5 = radii(1:n);
metricStrong5 = metric(1:n);
DIAMETER = radiiStrong5*0.09*2
viscircles(centersStrong5, radiiStrong5,'EdgeColor','r');
% Initialize 'feats' structure
feats=struct(...
'pos',[0 0],... % Centroid - [y x]
'ecc',0,... % Eccentricity
'ori',0); % Orientation
h = figure,imshow(I.*Igray,[]);
hold on
for j = 1:length(stats)
feats.pos(j,1) = stats(j).Centroid(1);
feats.pos(j,2) = stats(j).Centroid(2);
feats.ecc(j,1) = stats(j).Eccentricity;
feats.ori(j,1) = stats(j).Orientation;
feats.len(j,1) = stats(j).MajorAxisLength;
%aux2 = aux1(round(stats(j).Centroid(1)),round(stats(j).Centroid(2))),
x=stats(j).Centroid(1);
y=stats(j).Centroid(2);
%plot(x,y,'b*','LineWidth',5);
aux2 = sum([aux1(stats(j).PixelIdxList)]);
% text(x,y,[num2str(aux2)],'Color','b');
end
%list = find([stats.Area]>1600 & [stats.Area]<1705)
%list = find(Igray([stats.Centroid])>2000)
%list = find([stats.Area]>p1min);
%list = find([stats.MajorAxisLength]>80);
%x=[stats.Centroid(1)];
%y=[stats.Centroid(2)];
centroids = cat(1,stats.Centroid);
for i = 1: length(stats)
aux3(i)= aux1(round(centroids(i,1)),round(centroids(i,2)));
end
list = find([stats.Perimeter]>p2min & [stats.Perimeter]<p2max & [stats.Area]>p1min & [stats.Area]<p1max & [aux3]>p3min & [aux3]<p3max);
%for i = 1:length(stats)
% x=round(stats(i).Centroid(1));
% y=round(stats(i).Centroid(2));
% if Igray(x,y)>3600
% plot(x,y,'r*','LineWidth',2);
% end
%end
% PLOTS the segmentation figure with the aggregates
metrics = stats(1);%:length(list));
%statsAgg=0;
k=0;
% Open/create text files
fid=fopen([dirName,fileName(1:end-4),floor(now),'metrics.txt'],'a+');
fprintf(fid,'Selection based on (in microns): \n');
fprintf(fid,' MnAre | MxAre | MnPer | MxPer | Mn Ar/Pe \n');
fprintf(fid,'%6.0f %6.0f %6.0f %6.0f %6.1f \n',p1min*pxlSize*pxlSize,p1max*pxlSize*pxlSize,p2min*pxlSize,p2max*pxlSize,6.3);
fprintf(fid,' MnInt | MxInt \n');
fprintf(fid,'%6.0f %6.0f \n',p3min,p3max);
% fprintf(fid,'%6.0 %6.0 %6.0f %6.0f %6.0f %6.0f %6.1f \n',p3min,p3max,p1min*pxlSize*pxlSize,p1max*pxlSize*pxlSize,p2min*pxlSize,p2max*pxlSize,6.3);
for i = 1:length(list)
if stats(list(i)).Area/stats(list(i)).Perimeter>2%6.3%4%7.8
k=k+1;
x=stats(list(i)).Centroid(1);
y=stats(list(i)).Centroid(2);
%plot(x,y,'b*','LineWidth',5);
text(x+12,y+12,[num2str(round(stats(list(i)).Perimeter*pxlSize))],'Color','r');
text(x+50,y+50,[num2str(round(stats(list(i)).Area*pxlSize*pxlSize*10)/10)],'Color','g');
text(x+80,y+80,[num2str(aux1(round(x),round(y)))],'Color','y');% display aggregatte centroid intensity
fprintf(fid,'----------------------------------------------------------------\n');
fprintf(fid,' Area | Perim | MjAx | MnAx | Eccen | CentI | CentX | CentY \n');
fprintf(fid,'%6.1f %6.0f %6.0f %6.0f %6.2f %6.0f %6.0f %6.0f\n',stats(list(i)).Area*pxlSize*pxlSize,stats(list(i)).Perimeter*pxlSize,stats(list(i)).MajorAxisLength*pxlSize,stats(list(i)).MinorAxisLength*pxlSize,stats(list(i)).Eccentricity, aux1(round(x),round(y)),x,y);
metrics(k)=stats(list(i));
%writetable(struct2table(statistics), 'test.xls','sheet',k)
end
end
fprintf(fid,'----------------------------------------------------------------\n');
fprintf(fid,'The number of detected aggregates is:');
fprintf(fid,'%6.0f\n',k);
% Close text file
fclose(fid);
%plot(metrics(1).PixelList(1,:),'r*')
title([num2str(k),' aggregates detected, perimeter in microns (red), area in square microns (green), centroid intensity in arbitrary units (yellow)']);
save([dirName,fileName(1:end-4),floor(now),'metrics.mat'],'metrics');
hold off
saveas(h,[dirName,fileName(1:end-4),floor(now),'segmentedAggregates.tif']);
%writetable(struct2table(metrics), [dirName,filesep,'metrics.xlsx'])
% goodFeats = find(15<(feats.len));
% featNames = fieldnames(feats);
% for field = 1:length(featNames)
% feats.(featNames{field}) = feats.(featNames{field})(goodFeats,:);
% end