X-ray Image Assembler This Python script, powered by the Pillow library for image processing, is designed to assemble X-ray images of the human body, offering a versatile tool for visualizing medical data. The script facilitates the combination and arrangement of individual X-ray images, providing a comprehensive view for medical analysis or educational purposes.
Key Features: Opening and Saving X-ray Images:
Utilizes the Image.open() method to open individual X-ray images, enabling the inclusion of different views or anatomical regions. Employs the img.save(file) function to save the opened X-ray images, preserving the modified images for further reference or medical documentation. Combining X-ray Images Side by Side:
Implements sections of code to combine X-ray images side by side, creating larger composite images. Utilizes the Image.new() method to generate a new blank image with the combined width and pastes individual X-ray images onto it. Image Averaging (Incomplete):
Includes a section attempting to calculate the average of a set of X-ray images. Note that this part of the code seems incomplete and may require additional customization for specific medical imaging scenarios. Displaying Images:
Employs img.show() to display the assembled X-ray images, providing a visual representation of the combined or processed medical images using the default system image viewer. Example Use Case: Create comprehensive visualizations of medical X-ray data for diagnostic or educational purposes. Combine X-ray images from different perspectives to generate a holistic view of specific anatomical regions. Usage: Ensure that the Pillow library is installed (pip install Pillow). Adjust file paths and filenames according to your directory structure and X-ray image files. Run the script to generate composite X-ray images for medical analysis or educational presentations. Feel free to explore and customize the script to suit your specific medical imaging projects, always keeping in mind the ethical and privacy considerations associated with medical data.