Matlab code for ultrasound image segmentation.

 Matlab code for ultrasound image segmentation.

Ultrasound image segmentation is a process of separating regions of interest in an ultrasound image from the background. Here's an example of how to perform ultrasound image segmentation using the Otsu's thresholding method in MATLAB:

% Load ultrasound image img = imread('ultrasound_image.png'); % Convert image to grayscale img_gray = rgb2gray(img); % Compute Otsu's threshold threshold = graythresh(img_gray); % Segment image using threshold img_seg = imbinarize(img_gray, threshold); % Display segmented image imshow(img_seg); title('Segmented Image');

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 Otsu's threshold using the graythresh function. This function computes the threshold value that maximizes the variance between the foreground and background pixels in the image.

We then segment the image using the threshold value by applying the imbinarize function to the grayscale image. The imbinarize function converts the grayscale image to a binary image by thresholding the pixel values.

Finally, we display the segmented image using the imshow function.

Note that this is just one example of how to perform ultrasound image segmentation in MATLAB, and other techniques can be used depending on the specific requirements of the application.