Finding mean pixel value within boundaries

Question

Simon Kirkman 2020-6-24

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/553774-finding-mean-pixel-value-within-boundaries

评论： Image Analyst 2020-6-24

NDVI_1.png

I am trying to analyse the different NDVI pixel values of several different plants by getting the mean pixel value for each plant. I have used bwboundaries to find the boundaries of all the plants but i was wondering how you get the mean pixel value within each boundary. I have inserted the image and the code i have done to this point.

%%read in original image as grayscale
original = rgb2gray(imread("c:/Users/simon/Documents/ProjIM/Proj Im/ASI/2206-1-3/NDVI_1.png"))
imshow(original)
%% change to type double because of NVDI values and threshold
doubleImage = im2double(original)
threshValue = 0.05
binaryIm = doubleImage > threshValue
binaryIm = imfill(binaryIm,'holes')
imshow(binaryIm)
%%filter out 12 largest areas for 12 plants
filtered = bwareafilt(binaryIm,12,8)
imshow(filtered)
%% get boundaries with bwboundaries
[boundaries , labelled] = bwboundaries(filtered)
%% plot boundaries on original image to check they are correct
numberOfBoundaries = size(boundaries)
imshow(original)
hold on
for k = 1 : numberOfBoundaries
	thisBoundary = boundaries{k};
	plot(thisBoundary(:,2), thisBoundary(:,1), 'g', 'LineWidth', 2);
end
hold off

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Answer 1

Image Analyst 2020-6-24

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You can use mean():

[rows, columns] = size(original)
for k = 1 : numberOfBoundaries
    thisBoundary = boundaries{k};
    x = thisBoundary(:,2);
    y = thisBoundary(:,1)
    plot(x, y, 'g', 'LineWidth', 2);
    mask = poly2mask(x, y, rows, columns);
    theMeans(k) = mean(original(mask));
end

Or (much better), you can get the means for each blob from regionprops(), instead of using masks inside the loop:

props = regionprops(binaryIm, original, 'MeanIntensity');
theMeans = [props.MeanIntensity]

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Simon Kirkman 2020-6-24

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Thanks for this. I had read your freehand example and manged to peice together the first example with slightly different code that ill put below if anybody ever comes back to this. I am also going to give your second method a go and see how the values compare.

for n = 1:size(boundaries)
B = boundaries{n};
Bx = B(:,2);
By = B(:,1);
BW2 = poly2mask(Bx,By, rows, cols);
imshow (original);
blackmasked = doubleImage;
blackmasked(~BW2) = 0;
meanGL(n) = mean(blackmasked(BW2));
drawnow
end

Image Analyst 2020-6-24

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The line

blackmasked(~BW2) = 0;

is not needed since you're not looking outside the BW2 mask anyway. Get rid of it to save a very tiny bit of time. Plus you can take the imshow(original) and drawnow out of the loop and put it before since it doesn't change at all during the loop.

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Answer 2

Monalisa Pal 2020-6-24

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I am not sure whether my answer is the best way to do it but here's an attempt using the concept of connected component labelling:

%% getting mean within boundaries
[labelRegions, numberOfRegions] = bwlabel(filtered, 8); % using 8-connectivity
% Note that numberOfRegions == numberOfBoundaries
regionwiseMeanPixel = zeros(1, numberOfRegions);
for k = 1 : numberOfRegions
    mask = (L == k);
    region_k = uint8(mask) .* original;
    regionwiseMeanPixel(k) = sum(region_k(:)) / sum(mask(:));
end