Hey Ashfaq,
I understand that you want to calculate signal-to-noise (SNR) ratio using the average of 235 images and then perform the one-sided Student’s t-test with 5% significance (95% confidence).
Refer to the following code using a dummy data below:
% Dummy data generation
% 376x268x235, values ranging from -2.5 to 0.65
dummyData = -2.5 + (0.65 + 2.5) * rand(376, 268, 235);
% Calculate the Mean and Standard Error for Each Pixel
meanTemp = mean(dummyData, 3);
stdTemp = std(dummyData, 0, 3);
n = size(dummyData, 3);
seTemp = stdTemp / sqrt(n);
% Calculate the SNR for Each Pixel
snr = meanTemp ./ seTemp;
% Plot the SNR
figure;
imagesc(snr);
colorbar;
title('Signal-to-Noise Ratio (SNR)');
axis image;
I am assuming that you want to perform Student’s t-test with the null hypothesis that the data comes from normal distribution with mean equal to zero. Normally one would be using the “ttest” function given in the documentation as follows:
[h,~,~,stats] = ttest(x,0,"Alpha",0.05,"Tail","left")
Here the above line performs a t-test on sample "x" with mean 0 and 5% significance while specifying the direction for the one-sided test. The value of "h” represents whether the null hypothesis was accepted or rejected.
In your case, the signal-to-noise ratio is stored in a matrix. We can use signal-to-noise values (t-values) for comparison with "t-critical” values.
t_critical = tinv(0.95, n - 1);
statistical_Significance = abs(snr)>t_critical;
If the "t-value" lies in the bounds of “t-critical” then the null hypothesis is considered over the alternate hypothesis.
Hope this helps!
