Real-World Scenarios

import SimpleITK as sitk
import numpy as np

def medical_image_preprocessing(input_path, output_path):
    """
    Complete preprocessing pipeline for medical images.
    
    Steps:
    1. Read DICOM or NIfTI
    2. Denoise with anisotropic diffusion
    3. Normalize intensity
    4. Resample to isotropic spacing
    5. Bias field correction
    6. Save preprocessed image
    """
    
    # Read image
    image = sitk.ReadImage(input_path)
    print(f"Original size: {image.GetSize()}")
    print(f"Original spacing: {image.GetSpacing()}")
    
    # Cast to float for processing
    image = sitk.Cast(image, sitk.sitkFloat32)
    
    # Denoise with anisotropic diffusion
    image = sitk.CurvatureAnisotropicDiffusion(
        image,
        timeStep=0.0625,
        conductanceParameter=3.0,
        numberOfIterations=5
    )
    
    # Normalize intensity to [0, 1000]
    image = sitk.RescaleIntensity(image, outputMinimum=0, outputMaximum=1000)
    
    # Resample to isotropic spacing
    original_spacing = image.GetSpacing()
    original_size = image.GetSize()
    
    # Use minimum spacing for isotropic
    iso_spacing = [min(original_spacing)] * 3
    
    # Compute new size
    new_size = [
        int(original_size[i] * original_spacing[i] / iso_spacing[i])
        for i in range(3)
    ]
    
    image = sitk.Resample(
        image,
        size=new_size,
        transform=sitk.Transform(),
        interpolator=sitk.sitkLinear,
        outputOrigin=image.GetOrigin(),
        outputSpacing=iso_spacing,
        outputDirection=image.GetDirection(),
        defaultPixelValue=0
    )
    
    print(f"Resampled size: {image.GetSize()}")
    print(f"Resampled spacing: {image.GetSpacing()}")
    
    # Bias field correction (N4)
    image = sitk.N4BiasFieldCorrection(image, image > 0)
    
    # Save
    sitk.WriteImage(image, output_path)
    
    return image

import SimpleITK as sitk

def segment_brain_tumor(mri_path, seed_points, output_path):
    """
    Segment brain tumor from MRI using region growing and morphology.
    
    Args:
        mri_path: Path to MRI image
        seed_points: List of seed coordinates [(x,y,z), ...]
        output_path: Path to save segmentation
    """
    
    # Read and preprocess
    image = sitk.ReadImage(mri_path)
    image = sitk.Cast(image, sitk.sitkFloat32)
    
    # Denoise
    image = sitk.CurvatureAnisotropicDiffusion(
        image,
        timeStep=0.0625,
        conductanceParameter=3.0,
        numberOfIterations=5
    )
    
    # Initial segmentation with confidence connected
    initial_seg = sitk.ConfidenceConnected(
        image,
        seedList=seed_points,
        numberOfIterations=5,
        multiplier=2.5,
        initialNeighborhoodRadius=1
    )
    
    # Morphological cleanup
    # Remove small noise
    cleaned = sitk.BinaryMorphologicalOpening(
        initial_seg,
        kernelRadius=[2, 2, 2],
        kernelType=sitk.sitkBall
    )
    
    # Fill holes
    filled = sitk.BinaryFillhole(cleaned)
    
    # Smooth boundaries
    smoothed = sitk.BinaryMorphologicalClosing(
        filled,
        kernelRadius=[1, 1, 1],
        kernelType=sitk.sitkBall
    )
    
    # Label connected components
    labeled = sitk.ConnectedComponent(smoothed)
    
    # Keep only largest component
    labeled = sitk.RelabelComponent(labeled, minimumObjectSize=500)
    
    # Get statistics
    shape_stats = sitk.LabelShapeStatisticsImageFilter()
    shape_stats.Execute(labeled)
    
    if shape_stats.GetNumberOfLabels() > 0:
        label = shape_stats.GetLabels()[0]
        volume_mm3 = shape_stats.GetPhysicalSize(label)
        centroid = shape_stats.GetCentroid(label)
        
        print(f"Tumor volume: {volume_mm3:.2f} mm³")
        print(f"Tumor centroid: {centroid}")
    
    # Save segmentation
    sitk.WriteImage(labeled, output_path)
    
    return labeled

import SimpleITK as sitk

def ct_mri_registration(ct_path, mri_path, output_path):
    """
    Register CT to MRI using mutual information.
    
    Multi-modal registration requires mutual information metric.
    """
    
    # Read images
    fixed_ct = sitk.ReadImage(ct_path, sitk.sitkFloat32)
    moving_mri = sitk.ReadImage(mri_path, sitk.sitkFloat32)
    
    # Initialize rigid transform
    initial_transform = sitk.CenteredTransformInitializer(
        fixed_ct,
        moving_mri,
        sitk.Euler3DTransform(),
        sitk.CenteredTransformInitializerFilter.GEOMETRY
    )
    
    # Registration method
    registration = sitk.ImageRegistrationMethod()
    
    # Mutual information for multi-modal
    registration.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
    registration.SetMetricSamplingStrategy(registration.RANDOM)
    registration.SetMetricSamplingPercentage(0.01)
    
    # Optimizer
    registration.SetOptimizerAsGradientDescent(
        learningRate=1.0,
        numberOfIterations=100,
        convergenceMinimumValue=1e-6,
        convergenceWindowSize=10
    )
    registration.SetOptimizerScalesFromPhysicalShift()
    
    # Multi-resolution
    registration.SetShrinkFactorsPerLevel([4, 2, 1])
    registration.SetSmoothingSigmasPerLevel([2.0, 1.0, 0.0])
    registration.SetSmoothingSigmasAreSpecifiedInPhysicalUnits(True)
    
    registration.SetInterpolator(sitk.sitkLinear)
    registration.SetInitialTransform(initial_transform, inPlace=False)
    
    # Monitor progress
    def iteration_callback():
        if registration.GetOptimizerIteration() % 10 == 0:
            print(f"Iteration {registration.GetOptimizerIteration()}: "
                  f"Metric = {registration.GetMetricValue():.4f}")
    
    registration.AddCommand(sitk.sitkIterationEvent, iteration_callback)
    
    # Execute
    final_transform = registration.Execute(fixed_ct, moving_mri)
    
    # Apply transform
    registered_mri = sitk.Resample(
        moving_mri,
        fixed_ct,
        final_transform,
        sitk.sitkLinear,
        0.0,
        moving_mri.GetPixelID()
    )
    
    # Save
    sitk.WriteImage(registered_mri, output_path)
    
    # Save transform
    sitk.WriteTransform(final_transform, output_path.replace('.nii', '_transform.tfm'))
    
    return registered_mri, final_transform

import SimpleITK as sitk
import numpy as np

def detect_lung_nodules(ct_path):
    """
    Detect lung nodules in CT scan.
    
    Uses thresholding, morphology, and shape analysis.
    """
    
    # Read CT
    ct = sitk.ReadImage(ct_path)
    ct = sitk.Cast(ct, sitk.sitkFloat32)
    
    # Threshold for lung tissue (-1000 to -400 HU)
    lung_mask = sitk.BinaryThreshold(
        ct,
        lowerThreshold=-1000,
        upperThreshold=-400,
        insideValue=1,
        outsideValue=0
    )
    
    # Fill lung regions
    lung_mask = sitk.BinaryFillhole(lung_mask)
    
    # Threshold for potential nodules (-400 to 100 HU)
    nodule_candidates = sitk.BinaryThreshold(
        ct,
        lowerThreshold=-400,
        upperThreshold=100,
        insideValue=1,
        outsideValue=0
    )
    
    # Mask to lung region only
    nodule_candidates = sitk.And(nodule_candidates, lung_mask)
    
    # Label candidates
    labeled = sitk.ConnectedComponent(nodule_candidates)
    
    # Filter by shape
    shape_stats = sitk.LabelShapeStatisticsImageFilter()
    shape_stats.Execute(labeled)
    
    nodules = []
    for label in shape_stats.GetLabels():
        size = shape_stats.GetNumberOfPixels(label)
        roundness = shape_stats.GetRoundness(label)
        physical_size = shape_stats.GetPhysicalSize(label)
        
        # Filter criteria: size 50-5000 mm³, roundness > 0.5
        if 50 < physical_size < 5000 and roundness > 0.5:
            centroid = shape_stats.GetCentroid(label)
            nodules.append({
                'label': label,
                'volume_mm3': physical_size,
                'centroid': centroid,
                'roundness': roundness
            })
    
    print(f"Detected {len(nodules)} potential nodules")
    for i, nodule in enumerate(nodules):
        print(f"  Nodule {i+1}: {nodule['volume_mm3']:.1f} mm³ at {nodule['centroid']}")
    
    return nodules, labeled

import SimpleITK as sitk

def segment_cardiac_chambers(cardiac_mri_path):
    """
    Segment cardiac chambers from MRI using watershed.
    """
    
    # Read cardiac MRI
    image = sitk.ReadImage(cardiac_mri_path)
    image = sitk.Cast(image, sitk.sitkFloat32)
    
    # Smooth
    smoothed = sitk.SmoothingRecursiveGaussian(image, sigma=[1.0, 1.0, 1.0])
    
    # Compute gradient magnitude
    gradient = sitk.GradientMagnitudeRecursiveGaussian(smoothed, sigma=1.0)
    
    # Watershed segmentation
    labels = sitk.MorphologicalWatershed(
        gradient,
        level=0.1,
        markWatershedLine=False
    )
    
    # Analyze chambers
    shape_stats = sitk.LabelShapeStatisticsImageFilter()
    shape_stats.Execute(labels)
    
    intensity_stats = sitk.LabelIntensityStatisticsImageFilter()
    intensity_stats.Execute(image, labels)
    
    chambers = []
    for label in shape_stats.GetLabels():
        volume = shape_stats.GetPhysicalSize(label)
        mean_intensity = intensity_stats.GetMean(label)
        
        if volume > 1000:  # Filter small regions
            chambers.append({
                'label': label,
                'volume_mm3': volume,
                'mean_intensity': mean_intensity,
                'centroid': shape_stats.GetCentroid(label)
            })
    
    print(f"Identified {len(chambers)} cardiac chambers")
    
    return chambers, labels

import SimpleITK as sitk

def segment_bone_from_ct(ct_path, output_path):
    """
    Segment bone from CT using Hounsfield unit thresholding.
    
    Bone typically has HU > 200.
    """
    
    # Read CT
    ct = sitk.ReadImage(ct_path)
    
    # Threshold for bone (HU > 200)
    bone_mask = sitk.BinaryThreshold(
        ct,
        lowerThreshold=200,
        upperThreshold=3000,
        insideValue=1,
        outsideValue=0
    )
    
    # Morphological cleanup
    # Remove small fragments
    bone_mask = sitk.BinaryMorphologicalOpening(
        bone_mask,
        kernelRadius=[2, 2, 2],
        kernelType=sitk.sitkBall
    )
    
    # Fill small holes
    bone_mask = sitk.BinaryMorphologicalClosing(
        bone_mask,
        kernelRadius=[3, 3, 3],
        kernelType=sitk.sitkBall
    )
    
    # Label bones
    labeled = sitk.ConnectedComponent(bone_mask)
    labeled = sitk.RelabelComponent(labeled, minimumObjectSize=1000)
    
    # Analyze each bone
    shape_stats = sitk.LabelShapeStatisticsImageFilter()
    shape_stats.Execute(labeled)
    
    print(f"Segmented {shape_stats.GetNumberOfLabels()} bones")
    
    for label in shape_stats.GetLabels():
        volume = shape_stats.GetPhysicalSize(label)
        centroid = shape_stats.GetCentroid(label)
        print(f"  Bone {label}: {volume:.2f} mm³")
    
    # Save
    sitk.WriteImage(labeled, output_path)
    
    return labeled

import SimpleITK as sitk

def enhance_and_segment_vessels(angiography_path, output_path):
    """
    Enhance and segment blood vessels from angiography.
    
    Uses Hessian-based vessel enhancement.
    """
    
    # Read image
    image = sitk.ReadImage(angiography_path)
    image = sitk.Cast(image, sitk.sitkFloat32)
    
    # Smooth
    smoothed = sitk.SmoothingRecursiveGaussian(image, sigma=[0.5, 0.5, 0.5])
    
    # Hessian-based vessel enhancement
    # Note: This is a simplified approach
    # For production, use dedicated vessel enhancement filters
    
    # Multi-scale enhancement
    enhanced = None
    for sigma in [1.0, 2.0, 3.0]:
        hessian = sitk.HessianRecursiveGaussian(smoothed, sigma=sigma)
        # Process Hessian eigenvalues for vesselness
        # (simplified - actual implementation more complex)
    
    # Threshold enhanced image
    vessels = sitk.BinaryThreshold(
        smoothed,  # Using smoothed as proxy
        lowerThreshold=100,
        upperThreshold=1000
    )
    
    # Morphological cleanup
    vessels = sitk.BinaryMorphologicalOpening(
        vessels,
        kernelRadius=[1, 1, 1],
        kernelType=sitk.sitkBall
    )
    
    # Skeletonize vessels
    skeleton = sitk.BinaryThinning(vessels)
    
    # Save
    sitk.WriteImage(skeleton, output_path)
    
    return skeleton

import SimpleITK as sitk

def multi_atlas_segmentation(target_path, atlas_paths, atlas_label_paths, output_path):
    """
    Multi-atlas segmentation using label fusion.
    
    Args:
        target_path: Target image to segment
        atlas_paths: List of atlas image paths
        atlas_label_paths: List of atlas label paths
        output_path: Output segmentation path
    """
    
    # Read target
    target = sitk.ReadImage(target_path, sitk.sitkFloat32)
    
    registered_labels = []
    
    # Register each atlas to target
    for atlas_path, label_path in zip(atlas_paths, atlas_label_paths):
        print(f"Processing atlas: {atlas_path}")
        
        # Read atlas and labels
        atlas = sitk.ReadImage(atlas_path, sitk.sitkFloat32)
        labels = sitk.ReadImage(label_path)
        
        # Register atlas to target
        initial_transform = sitk.CenteredTransformInitializer(
            target,
            atlas,
            sitk.AffineTransform(3),
            sitk.CenteredTransformInitializerFilter.GEOMETRY
        )
        
        registration = sitk.ImageRegistrationMethod()
        registration.SetMetricAsMattesMutualInformation(50)
        registration.SetOptimizerAsGradientDescent(
            learningRate=1.0,
            numberOfIterations=100
        )
        registration.SetInterpolator(sitk.sitkLinear)
        registration.SetInitialTransform(initial_transform)
        
        final_transform = registration.Execute(target, atlas)
        
        # Warp labels to target space
        warped_labels = sitk.Resample(
            labels,
            target,
            final_transform,
            sitk.sitkNearestNeighbor,  # Use nearest neighbor for labels
            0,
            labels.GetPixelID()
        )
        
        registered_labels.append(warped_labels)
    
    # Label fusion using STAPLE
    consensus = sitk.STAPLEImageFilter()
    consensus.SetForegroundValue(1)
    fused = consensus.Execute(*registered_labels)
    
    # Threshold consensus
    final_segmentation = sitk.BinaryThreshold(
        fused,
        lowerThreshold=0.5,
        upperThreshold=1.0,
        insideValue=1,
        outsideValue=0
    )
    
    # Save
    sitk.WriteImage(final_segmentation, output_path)
    
    return final_segmentation

import SimpleITK as sitk
import numpy as np

def texture_based_classification(image_path, label_path):
    """
    Classify regions based on texture features.
    
    Uses local statistics and filtering.
    """
    
    # Read image and labels
    image = sitk.ReadImage(image_path)
    labels = sitk.ReadImage(label_path)
    
    # Compute texture features for each region
    intensity_stats = sitk.LabelIntensityStatisticsImageFilter()
    intensity_stats.Execute(image, labels)
    
    # Local gradient magnitude
    gradient = sitk.GradientMagnitude(image)
    gradient_stats = sitk.LabelIntensityStatisticsImageFilter()
    gradient_stats.Execute(gradient, labels)
    
    # Analyze each region
    features = []
    for label in intensity_stats.GetLabels():
        feature_dict = {
            'label': label,
            'mean_intensity': intensity_stats.GetMean(label),
            'std_intensity': intensity_stats.GetStandardDeviation(label),
            'mean_gradient': gradient_stats.GetMean(label),
            'std_gradient': gradient_stats.GetStandardDeviation(label)
        }
        features.append(feature_dict)
    
    return features

import SimpleITK as sitk
import numpy as np

def analyze_4d_timeseries(timeseries_path):
    """
    Analyze 4D time series (3D + time).
    
    Computes temporal statistics and motion.
    """
    
    # Read 4D image
    image_4d = sitk.ReadImage(timeseries_path)
    
    # Convert to NumPy for temporal analysis
    array_4d = sitk.GetArrayFromImage(image_4d)
    # Shape: (time, depth, height, width)
    
    num_timepoints = array_4d.shape[0]
    print(f"Time points: {num_timepoints}")
    
    # Compute temporal mean
    mean_over_time = np.mean(array_4d, axis=0)
    mean_image = sitk.GetImageFromArray(mean_over_time)
    
    # Compute temporal standard deviation
    std_over_time = np.std(array_4d, axis=0)
    std_image = sitk.GetImageFromArray(std_over_time)
    
    # Copy spatial metadata from first timepoint
    # Extract first timepoint
    first_timepoint = sitk.Extract(
        image_4d,
        size=list(image_4d.GetSize()[:3]) + [0],
        index=[0, 0, 0, 0]
    )
    
    mean_image.CopyInformation(first_timepoint)
    std_image.CopyInformation(first_timepoint)
    
    # Identify regions with high temporal variation
    high_variation = sitk.BinaryThreshold(
        std_image,
        lowerThreshold=np.percentile(std_over_time, 90),
        upperThreshold=std_over_time.max()
    )
    
    return mean_image, std_image, high_variation

import SimpleITK as sitk
import os
from pathlib import Path

def batch_process_images(input_dir, output_dir, processing_func):
    """
    Batch process all images in directory.
    
    Args:
        input_dir: Input directory path
        output_dir: Output directory path
        processing_func: Function to apply to each image
    """
    
    # Create output directory
    Path(output_dir).mkdir(parents=True, exist_ok=True)
    
    # Process each image
    for filename in os.listdir(input_dir):
        if filename.endswith(('.nii', '.nii.gz', '.mha', '.dcm')):
            input_path = os.path.join(input_dir, filename)
            output_path = os.path.join(output_dir, filename)
            
            try:
                print(f"Processing {filename}...")
                image = sitk.ReadImage(input_path)
                result = processing_func(image)
                sitk.WriteImage(result, output_path)
                print(f"  ✓ Saved to {output_path}")
                
            except Exception as e:
                print(f"  ✗ Error processing {filename}: {e}")

# Example usage
def my_processing(image):
    """Custom processing function."""
    image = sitk.SmoothingRecursiveGaussian(image, sigma=[1.0, 1.0, 1.0])
    image = sitk.RescaleIntensity(image, outputMinimum=0, outputMaximum=255)
    return sitk.Cast(image, sitk.sitkUInt8)

batch_process_images('input_scans/', 'processed_scans/', my_processing)

import SimpleITK as sitk
import numpy as np

def assess_image_quality(image_path):
    """
    Assess image quality metrics.
    
    Computes SNR, CNR, and other quality measures.
    """
    
    image = sitk.ReadImage(image_path)
    array = sitk.GetArrayFromImage(image)
    
    # Signal-to-Noise Ratio (simplified)
    signal = array[array > np.percentile(array, 50)].mean()
    noise = array[array < np.percentile(array, 10)].std()
    snr = signal / noise if noise > 0 else float('inf')
    
    # Contrast-to-Noise Ratio
    foreground = array[array > np.percentile(array, 75)].mean()
    background = array[array < np.percentile(array, 25)].mean()
    cnr = (foreground - background) / noise if noise > 0 else float('inf')
    
    # Compute statistics
    stats = sitk.StatisticsImageFilter()
    stats.Execute(image)
    
    quality_metrics = {
        'snr': snr,
        'cnr': cnr,
        'mean': stats.GetMean(),
        'std': stats.GetSigma(),
        'min': stats.GetMinimum(),
        'max': stats.GetMaximum()
    }
    
    print("Image Quality Metrics:")
    for key, value in quality_metrics.items():
        print(f"  {key.upper()}: {value:.2f}")
    
    return quality_metrics