Matlab code for feature extraction from an ultrasound medical image.

 Matlab code for feature extraction from an ultrasound medical image.

Feature extraction is an important step in medical image analysis and can help to identify regions of interest, quantify disease progression, and aid in diagnosis. Here is an example of how to extract features from an ultrasound medical image using the Gray-Level Co-occurrence Matrix (GLCM) method in MATLAB:

% Load ultrasound image img = imread('ultrasound_image.png'); % Convert image to grayscale img_gray = rgb2gray(img); % Compute GLCM features glcm = graycomatrix(img_gray, 'NumLevels', 256, 'Offset', [0 1], 'Symmetric', true); stats = graycoprops(glcm, {'Contrast', 'Correlation', 'Energy', 'Homogeneity'}); % Display GLCM features disp(['Contrast: ', num2str(stats.Contrast)]); disp(['Correlation: ', num2str(stats.Correlation)]); disp(['Energy: ', num2str(stats.Energy)]); disp(['Homogeneity: ', num2str(stats.Homogeneity)]);

In the code above, we first load the ultrasound image using the imread function. We then convert the image to grayscale using the rgb2gray function.

Next, we compute GLCM features using the graycomatrix and graycoprops functions. The graycomatrix function computes the GLCM matrix, which contains the co-occurrence frequencies of pixel pairs in the image. The graycoprops function computes statistics such as contrast, correlation, energy, and homogeneity from the GLCM matrix.

We then display the GLCM features using the disp function.

Note that this is just one example of how to extract features from an ultrasound medical image in MATLAB, and other techniques can be used depending on the specific requirements of the application. Additionally, it is often useful to extract features from multiple regions of interest in the image, rather than just the whole image, to obtain more accurate and relevant information.