HOW TO CALCULATE THE DICE SIMILARITY OF THE IMAGES SUBPLOT.

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Hi all, I have 2 data set logical images(binary images). EACH DATA SET HAVE 23 IMAGES. I want to check the dice similarity.
Below is the code for aorigional images.
%% first, read the image data and labelled images
clc
clear all
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'labelledimages');
imds = imageDatastore(imageDir);
% view data set images origional
figure
for i = 1:23
subplot(5,5,i)
I = readimage(imds,i);
imshow(I)
title('training labels')
end
The the second one images code is below
%% second, read the binary images after segmentation
dataSetDir1 = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir1 = fullfile(dataSetDir1,'bnwaftersegmentation');
imds1 = imageDatastore(imageDir1);
% view data set images origional
figure
for ii = 1:23
subplot(5,5,ii)
II = readimage(imds1,ii);
imshow(II)
title('binary labels')
end
Then i used code below to know the dice similarity, but the answer is 0
similarity = dice(I, II)
similarity =
0
But I try test just one image (let say image number 11), its work.
s = imread('11.png');
d = imread('11.png');
similarity = dice(s,d)
similarity =
0.15119
ANYONE CAN HELP ME HOW TO CALCULATE THE TOTAL DICE SIMILSRITY FOR ALL 23 IMAGES

Akzeptierte Antwort

yanqi liu
yanqi liu am 26 Okt. 2021
%% first, read the image data and labelled images
clc
clear all
dataSetDir = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir = fullfile(dataSetDir,'labelledimages');
imds = imageDatastore(imageDir);
% view data set images origional
Is = [];
figure
for i = 1:23
subplot(5,5,i)
I = readimage(imds,i);
Is{end+1} = I;
imshow(I)
title('training labels')
end
%% second, read the binary images after segmentation
dataSetDir1 = fullfile('C:\Users\Akmal\Desktop\I-131 256 28.02.2020\I-131 SPECT NEMA VALIDATION 01112019 256X256 26.09.2021 petang');
imageDir1 = fullfile(dataSetDir1,'bnwaftersegmentation');
imds1 = imageDatastore(imageDir1);
% view data set images origional
Is2 = [];
figure
for ii = 1:23
subplot(5,5,ii)
II = readimage(imds1,ii);
Is2{end+1} = II;
imshow(II)
title('binary labels')
end
%% compare the dice similarity for every slice, like 1 with 1, 2 with 2, 3 with 3....and so on till 23 with 23..
similarity = [];
for i = 1 : 23
similarity(i) = dice(Is{i}, Is2{i});
fprintf('the dice similarity for %d with %d is %.3f\n', i, i, similarity(i));
end
  2 Kommentare
mohd akmal masud
mohd akmal masud am 26 Okt. 2021
tq sir, its work.
sir, how to buy your book?
yanqi liu
yanqi liu am 27 Okt. 2021
thank you,may be amazon
https://www.amazon.cn/dp/B086ZXVX2K/qid=1635297216

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