stephanfward/ai-mri-analysis-dicom

Slicing logic can be beneficial for preprocessing DICOM images, especially in medical imaging tasks where certain sections of the image may contain more relevant information than others.

AI MRI Analysis DICOM

Slicing logic can be beneficial for preprocessing DICOM images, especially in medical imaging tasks where certain sections of the image may contain more relevant information than others. Here's a Jupyter Notebook example on performing such processes.

import pydicom
import numpy as np 
from PIL import Image

def preprocess_image(dicom_file):
  # Read the DICOM file
  dicom_data = pydicom.dcmread(dicom_file)

  # Extract pixel data
  pixel_array = dicom_data.pixel_array

  # Perform slicing to extract relevant sections of the image
  sliced_image = slice_image(pixel_array, slice_position=(100, 100), slice_size=(128, 128))

  # Perform additional preprocessing steps if needed
  processed_image = resize_image(sliced_image, target_size=(256, 256))

return processed_image

def slice_image(image, slice_position, slice_size):
  # Slice the image around the specified position and size
  x_start, y_start = slice_position
  x_end, y_end = x_start + slice_size[0], y_start + slice_size[1]
  sliced_image = image[x_start:x_end, y_start:y_end]
return sliced_image

def resize_image(image, target_size):
  # Resize the image to the target size
  img = Image.fromarray(image)
  resized_image = img.resize(target_size, resample=Image.BICUBIC)
  resized_array = np.array(resized_image)
return resized_array

Example usage:

dicom_file = 'path/to/your/dicom/file.dcm' processed_image = preprocess_image(dicom_file)

Example usage:

dicom_file = 'path/to/your/dicom/file.dcm' processed_image = preprocess_image(dicom_file) You can then further process the processed_image as needed for your application