tessl/pypi-simpleitk
SimpleITK is a simplified interface to the Insight Toolkit (ITK) for image registration and segmentation
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mathematical.mddocs/reference/filters/
Mathematical Filters
Pixel-wise mathematical operations including arithmetic, trigonometric, logarithmic, exponential, and comparison operations. All filters operate element-wise on pixel values.
Capabilities
Arithmetic Operations
Basic arithmetic operations on images.
# Addition
def Add(image1, image2):
"""Add two images pixel-wise."""
class AddImageFilter:
def Execute(self, image1, image2):
"""Returns image1 + image2"""
# Subtraction
def Subtract(image1, image2):
"""Subtract image2 from image1 pixel-wise."""
# Multiplication
def Multiply(image1, image2):
"""Multiply two images pixel-wise."""
# Division
def Divide(image1, image2):
"""Divide image1 by image2 pixel-wise."""
# Modulus
def Modulus(image1, image2):
"""Compute modulus (remainder) pixel-wise."""
# Power
def Pow(image, exponent: float):
"""Raise pixels to power."""
# Square
def Square(image):
"""Square each pixel value."""
# Sqrt
def Sqrt(image):
"""Square root of each pixel."""
# Abs
def Abs(image):
"""Absolute value of each pixel."""
# UnaryMinus
def UnaryMinus(image):
"""Negate each pixel value."""Exponential and Logarithmic
def Exp(image):
"""Compute e^pixel for each pixel."""
def Log(image):
"""Compute natural logarithm of each pixel."""
def Log10(image):
"""Compute base-10 logarithm of each pixel."""Trigonometric Functions
def Sin(image):
"""Compute sine of each pixel (radians)."""
def Cos(image):
"""Compute cosine of each pixel (radians)."""
def Tan(image):
"""Compute tangent of each pixel."""
def Asin(image):
"""Compute arcsine of each pixel."""
def Acos(image):
"""Compute arccosine of each pixel."""
def Atan(image):
"""Compute arctangent of each pixel."""
def Atan2(image_y, image_x):
"""
Compute two-argument arctangent (atan2) pixel-wise.
Args:
image_y: Y coordinates
image_x: X coordinates
Returns:
Angle in radians for each pixel pair
"""Comparison Operations
Generate binary (true/false) images from comparisons.
def Greater(image1, image2):
"""Return image1 > image2 pixel-wise."""
def GreaterEqual(image1, image2):
"""Return image1 >= image2 pixel-wise."""
def Less(image1, image2):
"""Return image1 < image2 pixel-wise."""
def LessEqual(image1, image2):
"""Return image1 <= image2 pixel-wise."""
def Equal(image1, image2):
"""Return image1 == image2 pixel-wise."""
def NotEqual(image1, image2):
"""Return image1 != image2 pixel-wise."""Logical Operations
Boolean operations on binary images.
def And(image1, image2):
"""Logical AND of two images."""
def Or(image1, image2):
"""Logical OR of two images."""
def Xor(image1, image2):
"""Logical XOR of two images."""
def Not(image):
"""Logical NOT of image."""
def BitwiseNot(image):
"""Bitwise NOT of image."""Minimum and Maximum
def Maximum(image1, image2):
"""Element-wise maximum of two images."""
def Minimum(image1, image2):
"""Element-wise minimum of two images."""
def Clamp(image, lowerBound: float, upperBound: float):
"""
Clamp pixel values to range.
Args:
image: Input image
lowerBound: Minimum value
upperBound: Maximum value
Returns:
Image with values in [lowerBound, upperBound]
"""Usage Examples
Arithmetic Operations
import SimpleITK as sitk
img1 = sitk.ReadImage('image1.png')
img2 = sitk.ReadImage('image2.png')
# Using procedural interface
sum_img = sitk.Add(img1, img2)
diff_img = sitk.Subtract(img1, img2)
product_img = sitk.Multiply(img1, img2)
# Using operators (equivalent)
sum_img = img1 + img2
diff_img = img1 - img2
product_img = img1 * img2Mathematical Transformations
import SimpleITK as sitk
image = sitk.ReadImage('input.nii')
# Logarithmic transformation for visualization
log_img = sitk.Log(image + 1) # Add 1 to avoid log(0)
# Exponential transformation
exp_img = sitk.Exp(image * 0.01)
# Power transformation (gamma correction)
gamma_corrected = sitk.Pow(image, 0.5)Creating Masks with Comparisons
import SimpleITK as sitk
image = sitk.ReadImage('brain.nii')
# Create binary masks using comparisons
mask_high = sitk.Greater(image, 500)
mask_low = sitk.Less(image, 100)
# Combine masks with logical operations
mask_range = sitk.And(
sitk.Greater(image, 100),
sitk.Less(image, 500)
)
# Apply mask
masked_image = sitk.Multiply(image, sitk.Cast(mask_range, image.GetPixelID()))Trigonometric Operations
import SimpleITK as sitk
import math
# Create phase image
phase_img = sitk.ReadImage('phase.nii')
# Compute sine and cosine for phase unwrapping
sin_phase = sitk.Sin(phase_img)
cos_phase = sitk.Cos(phase_img)
# Reconstruct phase from components
unwrapped_phase = sitk.Atan2(sin_phase, cos_phase)Install with Tessl CLI
npx tessl i tessl/pypi-simpleitk