preprocessing.py

"""
Preprocessing script for DICOM medical images
"""
import os
import numpy as np
import pydicom
import cv2
from glob import glob
from tqdm import tqdm
import argparse

def apply_window_level(image, window_center, window_width):
    """
    Apply windowing to the DICOM image to enhance visualization
    
    Args:
        image (numpy.ndarray): Input image
        window_center (float): Window center (level)
        window_width (float): Window width
        
    Returns:
        numpy.ndarray: Windowed image
    """
    img_min = window_center - window_width // 2
    img_max = window_center + window_width // 2
    
    windowed = np.clip(image, img_min, img_max)
    windowed = (windowed - img_min) / (img_max - img_min) * 255.0
    
    return windowed.astype(np.uint8)

def process_dicom_files(input_dir, output_dir):
    """
    Process all DICOM files in the input directory
    
    Args:
        input_dir (str): Directory containing DICOM files
        output_dir (str): Directory to save processed images
    """
    # Create output directories
    os.makedirs(output_dir, exist_ok=True)
    images_dir = os.path.join(output_dir, "images")
    os.makedirs(images_dir, exist_ok=True)
    
    # Find all DICOM files
    dicom_files = glob(os.path.join(input_dir, "**", "*.dcm"), recursive=True)
    print(f"Found {len(dicom_files)} DICOM files")
    
    # Process each file
    for i, dicom_path in enumerate(tqdm(dicom_files, desc="Processing DICOM files")):
        try:
            # Read DICOM file
            dicom = pydicom.dcmread(dicom_path)
            
            # Extract image data
            image = dicom.pixel_array
            
            # Apply windowing if available
            if hasattr(dicom, 'WindowCenter') and hasattr(dicom, 'WindowWidth'):
                window_center = dicom.WindowCenter
                window_width = dicom.WindowWidth
                
                # Handle multiple window values
                if isinstance(window_center, pydicom.multival.MultiValue):
                    window_center = window_center[0]
                if isinstance(window_width, pydicom.multival.MultiValue):
                    window_width = window_width[0]
                    
                image = apply_window_level(image, window_center, window_width)
            else:
                # Apply default windowing for CT images
                if dicom.Modality == "CT":
                    image = apply_window_level(image, 40, 400)  # Soft tissue window
                else:
                    # Normalize to 0-255 range
                    image = ((image - image.min()) / (image.max() - image.min() + 1e-8) * 255).astype(np.uint8)
            
            # Generate output filename
            patient_id = dicom.PatientID if hasattr(dicom, 'PatientID') else "unknown"
            series_uid = dicom.SeriesInstanceUID if hasattr(dicom, 'SeriesInstanceUID') else "unknown"
            instance_uid = dicom.SOPInstanceUID if hasattr(dicom, 'SOPInstanceUID') else str(i)
            
            output_filename = f"{patient_id}_{series_uid[-8:]}_{instance_uid[-8:]}.png"
            output_path = os.path.join(images_dir, output_filename)
            
            # Save image
            cv2.imwrite(output_path, image)
            
            # Save metadata
            with open(os.path.join(output_dir, "metadata.txt"), "a") as f:
                f.write(f"{output_filename},{dicom_path}\n")
                
        except Exception as e:
            print(f"Error processing {dicom_path}: {e}")
    
    print(f"Processing complete. Processed images saved to {images_dir}")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Process DICOM files')
    parser.add_argument('--input', type=str, required=True, help='Input directory with DICOM files')
    parser.add_argument('--output', type=str, default='./processed_data', help='Output directory')
    
    args = parser.parse_args()
    
    process_dicom_files(args.input, args.output)