simpleitk

import SimpleITK as sitk # Create a 2D image (e.g., 64x64 pixels, 16-bit integer) image_size = [64, 64] image = sitk.Image(image_size, sitk.sitkInt16) # Set some pixel values (e.g., a square in the middle) # Note: SimpleITK indexing is (x,y,z), while numpy conversion often reverses axes. image[10:50, 10:50] = 100 # Apply a Gaussian smoothing filter gaussian_filter = sitk.SmoothingRecursiveGaussianImageFilter() gaussian_filter.SetSigma(2.0) # Set the standard deviation of the Gaussian smoothed_image = gaussian_filter.Execute(image) # Get a pixel value at a specific index pixel_value = smoothed_image.GetPixel((32, 32)) print(f"Pixel value at (32,32) after smoothing: {pixel_value}") # Save the image (e.g., to a MetaImage file format) sitk.WriteImage(smoothed_image, "smoothed_image.mha") print("Saved smoothed_image.mha") # To visualize the image, you would typically use an external viewer # For example, if ImageJ/Fiji is installed and configured: # sitk.Show(smoothed_image, 'Smoothed Image')