connected component analysis in MATLAB - matlab

Question

I want to apply connected component analysis on a grey scale image with considering pixels whose grey level is more than a threshold. then, I want to remove those connected components whose length is less than a threshold. please help me? I wrote following code in MATLAB, is it efficient?
thank you in advance.
%im = input image;
% alpha1 = 0.0001;
% alpha2 = 0.0001;
% [row col] = size(im);
%
%
% thr1 = mean(mean(im))-alpha1*std(std(im));
% BW = zeros(size(im));
%
% for rr = 1:row
% for cc = 1:col
% if im(rr,cc)>thr2
% BW(rr,cc) = 1;
% else
% BW(rr,cc) = 0;
% end
% end
% end
%
% CC = bwconncomp(BW);
% area_in_pixels = cellfun(#length,CC.PixelIdxList);
% thr2 = mean(area_in_pixels)-alpha2*std(area_in_pixels);
% idx = find(area_in_pixels <= thr3);
% for kk = 1:length(idx)
% aaa = idx(kk);
% BW(CC.PixelIdxList{aaa})=0;
% end

Answer 1

You can try regionprops instead to extract all objects in your image. With the code below you get the positions of all objects smaller than a threshold which you can manipulate or do what you need to do afterwards...
Comparably you can go through the different objects and extract the grey level and if it is below a threshold manipulate them.
% Threshold for the size in pixels that you want
threshold = 100;
% read your image
rawimage = imread('yourimage.jpg');
% create a 2D field by summing
im = sum(rawimage,3);
% label all objects that have 8 neighbours
IMAGE_labeled = bwlabel(im,8);
% get the properties of all elements
shapedata=regionprops (IMAGE_labeled,'all');
% get those elements that are smaller in size (area) than the threshold
index = find(cell2mat({shapedata(:).Area})<=threshold);
% make a contourplot of im
figure
contourf(im)
hold on
% creation of colormap with the size of all identified objects below the thres
mycolormap = jet(size(index,2));
% loop over all small objects, extraction of their position in the original file, plotting circles with different colors at the position of each small object
imap = 1;
mean_of_red = zeros(length(index),1);
for i = index
plot (shapedata(i).PixelList(:,1),shapedata(i).PixelList(:,2),'o','MarkerFaceColor',mycolormap(imap,:))
mean_of_red(i) = mean(mean(im(shapedata(i).PixelList(:,1),shapedata(i).PixelList(:,1),1)));
imap=imap+1;
end