How to calculate variance, kurtosis, and skewness of a ROI (Region of Interest) in grayscale image?

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Yogi Jefani
Yogi Jefani 2017-7-12
回答： Image Analyst 2024-12-29
I'm going to calculate means, standard deviation, kurtosis, skewness, and also variance from a ROI of grayscale image. I create select the ROI using mask. This is the code I use
Img = imread('image_name.jpg');
GrayImg = rgb2gray(Img);
h = impoly(gca);
wait(h);
mask = createMask(h);
Mean = mean2(GrayImg(mask));
StandardDeviation = std2(GrayImg(mask));
How to calculate variance, kurtosis and skewness of the grayscale image using the mask? Is the code to calculate mean and standard deviation is correct?

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回答（2 个）

Image Analyst
Image Analyst 2024-12-29
See my attached demo for computing image moments. Adapt as needed.
ag
ag 2024-12-29
Hi Yogi,
To calculate the statistical measures such as mean, standard deviation, variance, kurtosis, and skewness for a region of interest (ROI) in a grayscale image, you can utilize a mask to define the ROI. Below is an improved version of your code, which accurately performs these calculations:
% Read the image and convert it to grayscale
Img = imread('image_name.jpg');
GrayImg = rgb2gray(Img);
% Select the ROI using a polygon tool
h = impoly(gca);
wait(h);
% Generate a mask from the selected polygon
mask = createMask(h);
% Extract the pixel values within the mask
maskedPixels = GrayImg(mask);
% Compute the statistical measures
Mean = mean(maskedPixels);
StandardDeviation = std(maskedPixels);
Variance = var(maskedPixels);
Kurtosis = kurtosis(double(maskedPixels)); % Convert to double for kurtosis
Skewness = skewness(double(maskedPixels)); % Convert to double for skewness
% Display the results
For more details, please refer to the following MathWorks documentations:
Hope this helps!

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