Edge Cases and Special Scenarios

import SimpleITK as sitk

# Create empty image
empty = sitk.Image()

# Check if empty
if empty.GetSize() == ():
    print("Image is empty")

# Initialize empty image
empty = sitk.Image(256, 256, sitk.sitkUInt8)
# All pixels initialized to 0

# Single pixel image
single = sitk.Image(1, 1, sitk.sitkFloat32)
single.SetPixel(0, 0, 42.0)

# Filtering single-pixel images
# Most filters will work but may produce unexpected results
result = sitk.SmoothingRecursiveGaussian(single, sigma=[1.0, 1.0])

import SimpleITK as sitk
import numpy as np

# Image with NaN or Inf values
array = np.random.randn(100, 100, 50).astype(np.float32)
array[50, 50, 25] = np.nan
array[60, 60, 30] = np.inf

image = sitk.GetImageFromArray(array)

# Check for invalid values
stats = sitk.StatisticsImageFilter()
stats.Execute(image)

if np.isnan(stats.GetMean()) or np.isinf(stats.GetMean()):
    print("Image contains NaN or Inf values")
    
    # Clean up
    array = sitk.GetArrayFromImage(image)
    array = np.nan_to_num(array, nan=0.0, posinf=1000.0, neginf=-1000.0)
    image = sitk.GetImageFromArray(array)

# Extreme value handling
image = sitk.ReadImage('extreme_values.nii')

# Clip to reasonable range
image = sitk.Clamp(image, lowerBound=-10000, upperBound=10000)

# Or use robust normalization
stats = sitk.StatisticsImageFilter()
stats.Execute(image)

# Use percentiles for robust normalization
array = sitk.GetArrayFromImage(image)
p1, p99 = np.percentile(array, [1, 99])

image_clipped = sitk.Clamp(image, lowerBound=p1, upperBound=p99)
image_normalized = sitk.RescaleIntensity(image_clipped, outputMinimum=0, outputMaximum=1000)

import SimpleITK as sitk

# Image with highly anisotropic spacing
image = sitk.ReadImage('anisotropic.nii')
spacing = image.GetSpacing()
print(f"Spacing: {spacing}")  # e.g., (0.5, 0.5, 5.0)

# Resample to isotropic
iso_spacing = [min(spacing)] * 3

original_size = image.GetSize()
new_size = [
    int(original_size[i] * spacing[i] / iso_spacing[i])
    for i in range(3)
]

isotropic = sitk.Resample(
    image,
    size=new_size,
    transform=sitk.Transform(),
    interpolator=sitk.sitkLinear,
    outputOrigin=image.GetOrigin(),
    outputSpacing=iso_spacing,
    outputDirection=image.GetDirection()
)

# Image with oblique orientation
image = sitk.ReadImage('oblique.nii')
direction = image.GetDirection()

# Check if orthogonal
is_orthogonal = all(
    abs(direction[i*3 + j] - (1 if i == j else 0)) < 0.01
    for i in range(3) for j in range(3)
)

if not is_orthogonal:
    print("Image has oblique orientation")
    
    # Resample to standard orientation
    identity_direction = (1, 0, 0, 0, 1, 0, 0, 0, 1)
    
    resampled = sitk.Resample(
        image,
        size=image.GetSize(),
        transform=sitk.Transform(),
        interpolator=sitk.sitkLinear,
        outputOrigin=image.GetOrigin(),
        outputSpacing=image.GetSpacing(),
        outputDirection=identity_direction
    )

import SimpleITK as sitk

# Images with different sizes
image1 = sitk.ReadImage('image1.nii')  # Size: (256, 256, 100)
image2 = sitk.ReadImage('image2.nii')  # Size: (128, 128, 50)

# Cannot directly operate on them
# image_sum = image1 + image2  # ERROR!

# Solution: Resample to common geometry
image2_resampled = sitk.Resample(
    image2,
    image1,  # Use image1 as reference
    sitk.Transform(),
    sitk.sitkLinear,
    0.0,
    image2.GetPixelID()
)

# Now can operate
image_sum = image1 + image2_resampled

# Images with different pixel types
uint8_image = sitk.ReadImage('image1.png')  # UInt8
float_image = sitk.ReadImage('image2.mha')  # Float32

# Cannot directly operate
# result = uint8_image + float_image  # ERROR!

# Solution: Cast to common type
uint8_as_float = sitk.Cast(uint8_image, sitk.sitkFloat32)
result = uint8_as_float + float_image

# Cast back if needed
result_uint8 = sitk.Cast(result, sitk.sitkUInt8)

import SimpleITK as sitk
import numpy as np

def process_large_image_chunked(large_image_path, output_path):
    """
    Process very large image in chunks to avoid memory issues.
    """
    
    # Read image
    image = sitk.ReadImage(large_image_path)
    size = image.GetSize()
    
    # Process in slabs
    slab_size = 20  # Process 20 slices at a time
    
    results = []
    for z_start in range(0, size[2], slab_size):
        z_end = min(z_start + slab_size, size[2])
        
        # Extract slab
        extract_size = list(size[:2]) + [z_end - z_start]
        extract_index = [0, 0, z_start]
        
        slab = sitk.Extract(image, size=extract_size, index=extract_index)
        
        # Process slab
        processed_slab = sitk.SmoothingRecursiveGaussian(
            slab,
            sigma=[1.0, 1.0, 1.0]
        )
        
        results.append(processed_slab)
    
    # Join slabs back together
    final = sitk.JoinSeries(*results)
    final.CopyInformation(image)
    
    sitk.WriteImage(final, output_path)
    return final

def compute_statistics_efficiently(large_image_path):
    """Compute statistics without copying large image."""
    
    image = sitk.ReadImage(large_image_path)
    
    # Use view for read-only operations
    array_view = sitk.GetArrayViewFromImage(image)
    
    # Compute statistics efficiently
    stats = {
        'mean': array_view.mean(),
        'std': array_view.std(),
        'min': array_view.min(),
        'max': array_view.max(),
        'median': np.median(array_view),
        'percentiles': np.percentile(array_view, [25, 50, 75])
    }
    
    return stats

import SimpleITK as sitk

def read_specific_dicom_series(dicom_dir, series_description=None):
    """
    Read specific DICOM series from directory with multiple series.
    """
    
    # Get all series IDs
    series_ids = sitk.ImageSeriesReader.GetGDCMSeriesIDs(dicom_dir)
    print(f"Found {len(series_ids)} series")
    
    # Read metadata to find desired series
    for series_id in series_ids:
        reader = sitk.ImageSeriesReader()
        dicom_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(
            dicom_dir,
            series_id
        )
        reader.SetFileNames(dicom_names)
        
        # Read just metadata
        reader.LoadPrivateTagsOn()
        
        # Check series description if provided
        if series_description:
            # Read first file metadata
            file_reader = sitk.ImageFileReader()
            file_reader.SetFileName(dicom_names[0])
            file_reader.ReadImageInformation()
            
            if file_reader.HasMetaDataKey('0008|103e'):
                desc = file_reader.GetMetaData('0008|103e')
                if series_description.lower() in desc.lower():
                    print(f"Found matching series: {desc}")
                    return reader.Execute()
        else:
            # Return first series
            return reader.Execute()
    
    return None

def handle_missing_dicom_slices(dicom_dir):
    """Handle DICOM series with missing slices."""
    
    reader = sitk.ImageSeriesReader()
    dicom_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(dicom_dir)
    
    # Check for gaps in slice positions
    positions = []
    for filename in dicom_names:
        file_reader = sitk.ImageFileReader()
        file_reader.SetFileName(filename)
        file_reader.ReadImageInformation()
        
        if file_reader.HasMetaDataKey('0020|1041'):  # Slice Location
            pos = float(file_reader.GetMetaData('0020|1041'))
            positions.append(pos)
    
    # Check for gaps
    positions_sorted = sorted(positions)
    gaps = []
    for i in range(len(positions_sorted) - 1):
        diff = positions_sorted[i+1] - positions_sorted[i]
        if diff > 1.5 * (positions_sorted[1] - positions_sorted[0]):
            gaps.append(i)
    
    if gaps:
        print(f"WARNING: Found {len(gaps)} gaps in slice positions")
    
    # Read series
    reader.SetFileNames(dicom_names)
    volume = reader.Execute()
    
    return volume

import SimpleITK as sitk

# Float to integer conversion
float_image = sitk.Image(100, 100, sitk.sitkFloat32)
# Set fractional values
float_image.SetPixel(50, 50, 42.7)

# Cast to integer (truncates)
int_image = sitk.Cast(float_image, sitk.sitkInt16)
value = int_image.GetPixel(50, 50)  # 42 (not 43)

# For rounding, add 0.5 before casting
rounded = float_image + 0.5
int_rounded = sitk.Cast(rounded, sitk.sitkInt16)

# Integer overflow
uint8_image = sitk.Image(100, 100, sitk.sitkUInt8)
uint8_image.SetPixel(50, 50, 200)

# Addition can overflow
result = uint8_image + 100  # 200 + 100 = 300, but UInt8 max is 255

# Solution: Cast to larger type first
float_image = sitk.Cast(uint8_image, sitk.sitkFloat32)
result = float_image + 100
result = sitk.Clamp(result, lowerBound=0, upperBound=255)
result = sitk.Cast(result, sitk.sitkUInt8)

def register_different_modalities(fixed_ct, moving_pet):
    """
    Register images with vastly different intensity distributions.
    
    Use mutual information, not mean squares.
    """
    
    # Normalize both images first
    fixed_normalized = sitk.RescaleIntensity(fixed_ct, outputMinimum=0, outputMaximum=1000)
    moving_normalized = sitk.RescaleIntensity(moving_pet, outputMinimum=0, outputMaximum=1000)
    
    # Use mutual information
    registration = sitk.ImageRegistrationMethod()
    registration.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
    
    # ... rest of registration setup
    
    return registration.Execute(fixed_normalized, moving_normalized)

# Very small image (< 10 pixels per dimension)
tiny_image = sitk.Image(5, 5, sitk.sitkFloat32)

# Many filters require minimum size
# Pad to minimum size before filtering
padded = sitk.ConstantPad(
    tiny_image,
    padLowerBound=(10, 10),
    padUpperBound=(10, 10),
    constant=0
)

# Now can apply filters
smoothed = sitk.SmoothingRecursiveGaussian(padded, sigma=[1.0, 1.0])

# Crop back to original size
result = sitk.Crop(
    smoothed,
    lowerBoundaryCropSize=(10, 10),
    upperBoundaryCropSize=(10, 10)
)

def robust_region_growing(image, initial_seeds, lower, upper):
    """
    Region growing with fallback if seeds are outside region.
    """
    
    # Check if seeds are in valid range
    valid_seeds = []
    for seed in initial_seeds:
        value = image.GetPixel(*seed)
        if lower <= value <= upper:
            valid_seeds.append(seed)
    
    if not valid_seeds:
        print("WARNING: No seeds in valid intensity range")
        # Fallback: Use automatic threshold
        return sitk.BinaryThreshold(image, lowerThreshold=lower, upperThreshold=upper)
    
    # Proceed with region growing
    return sitk.ConnectedThreshold(
        image,
        seedList=valid_seeds,
        lower=lower,
        upper=upper
    )

def segment_multiple_disconnected_regions(image, seed_points):
    """
    Segment multiple disconnected regions.
    """
    
    # Segment each seed separately
    segments = []
    for i, seed in enumerate(seed_points):
        seg = sitk.ConfidenceConnected(
            image,
            seedList=[seed],
            numberOfIterations=5,
            multiplier=2.5
        )
        
        # Label this segment with unique ID
        seg = sitk.Cast(seg, sitk.sitkUInt32)
        seg = seg * (i + 1)
        
        segments.append(seg)
    
    # Combine all segments
    combined = segments[0]
    for seg in segments[1:]:
        combined = sitk.Maximum(combined, seg)
    
    return combined

import SimpleITK as sitk

# Some transforms cannot be inverted
affine = sitk.AffineTransform(3)

# Singular matrix (not invertible)
singular_matrix = (
    1, 0, 0,
    0, 1, 0,
    0, 0, 0  # Degenerate
)
affine.SetMatrix(singular_matrix)

try:
    inverse = affine.GetInverse()
except RuntimeError as e:
    print(f"Cannot invert: {e}")
    # Handle non-invertible case

def handle_displacement_field_boundaries(image, displacement_field):
    """
    Handle pixels that map outside image bounds.
    """
    
    # Warp with explicit boundary handling
    warped = sitk.Warp(
        image,
        displacement_field,
        interpolator=sitk.sitkLinear,
        edgePaddingValue=0  # Value for out-of-bounds pixels
    )
    
    return warped

import SimpleITK as sitk
import numpy as np

def dangerous_view_pattern():
    """WRONG: View outlives image."""
    
    def get_view():
        image = sitk.ReadImage('temp.nii')
        return sitk.GetArrayViewFromImage(image)
        # image deleted when function returns!
    
    view = get_view()
    # view now points to deleted memory - CRASH!
    # mean = view.mean()  # DANGER!

def safe_view_pattern():
    """CORRECT: Keep image alive."""
    
    image = sitk.ReadImage('temp.nii')
    view = sitk.GetArrayViewFromImage(image)
    
    # Use view while image is alive
    mean = view.mean()
    
    # If need to return, use copy
    return sitk.GetArrayFromImage(image)

# 4D array could be 3D vector or 4D scalar
array_4d = np.random.randn(3, 100, 100, 50).astype(np.float32)

# Explicit control
vector_image = sitk.GetImageFromArray(array_4d, isVector=True)
# Result: 3D image with 3 components per pixel

scalar_image = sitk.GetImageFromArray(array_4d, isVector=False)
# Result: 4D scalar image

# Zero sigma means no smoothing
image = sitk.ReadImage('input.nii')

# This returns the input unchanged
result = sitk.SmoothingRecursiveGaussian(image, sigma=[0.0, 0.0, 0.0])
# result is identical to image

# Lower > upper creates empty result
image = sitk.ReadImage('input.nii')

# This produces all zeros
empty = sitk.BinaryThreshold(
    image,
    lowerThreshold=200,
    upperThreshold=100  # Invalid: lower > upper
)

# Check for empty result
stats = sitk.StatisticsImageFilter()
stats.Execute(empty)
if stats.GetSum() == 0:
    print("WARNING: Threshold produced empty result")

# All-zero image
zero_image = sitk.Image(100, 100, sitk.sitkUInt8)

# Connected component on empty image
labeled = sitk.ConnectedComponent(zero_image)

# Check result
cc_filter = sitk.ConnectedComponentImageFilter()
cc_filter.Execute(zero_image)
num_objects = cc_filter.GetObjectCount()
print(f"Objects found: {num_objects}")  # 0

import SimpleITK as sitk

def safe_read_image(file_path):
    """Safely read image with format validation."""
    
    try:
        image = sitk.ReadImage(file_path)
        return image
        
    except RuntimeError as e:
        if "could not be created" in str(e).lower():
            print(f"Unsupported format: {file_path}")
            
            # Try specifying ImageIO explicitly
            for io_name in sitk.GetRegisteredImageIOs():
                try:
                    image = sitk.ReadImage(file_path, imageIO=io_name)
                    print(f"Success with {io_name}")
                    return image
                except:
                    continue
        
        raise

def validate_image_file(file_path):
    """Validate image file before full read."""
    
    try:
        # Read just metadata
        reader = sitk.ImageFileReader()
        reader.SetFileName(file_path)
        reader.ReadImageInformation()
        
        # Check basic properties
        size = reader.GetSize()
        if any(s <= 0 for s in size):
            print(f"Invalid size: {size}")
            return False
        
        # Check pixel type
        pixel_id = reader.GetPixelID()
        if pixel_id == sitk.sitkUnknown:
            print("Unknown pixel type")
            return False
        
        # Looks valid, read full image
        image = reader.Execute()
        return True
        
    except RuntimeError as e:
        print(f"Validation failed: {e}")
        return False

import SimpleITK as sitk

# Avoid division by zero
denominator = sitk.ReadImage('denominator.nii')

# Add small epsilon to avoid division by zero
epsilon = 1e-10
safe_denominator = denominator + epsilon

result = numerator / safe_denominator

# Log requires positive values
image = sitk.ReadImage('input.nii')

# Shift to ensure positive
min_val = sitk.MinimumMaximumImageFilter()
min_val.Execute(image)

if min_val.GetMinimum() <= 0:
    # Shift to positive range
    image = image - min_val.GetMinimum() + 1.0

# Now safe to take log
log_image = sitk.Log(image)