Simpleitk

Overview

SimpleITK simplifies the Insight Toolkit (ITK) for medical image processing: segmentation, registration, filtering, resampling, and morphological operations. Supports DICOM, NIfTI, NRRD, and 50+ file formats.

Installation

uv pip install SimpleITK

Basic Image Operations

import SimpleITK as sitk
import numpy as np

image = sitk.ReadImage("ct_scan.nii.gz")
print(image.GetSize(), image.GetSpacing(), image.GetOrigin())

array = sitk.GetArrayFromImage(image)
print(array.shape)  # (z, y, x)

Segmentation

binary = sitk.BinaryThreshold(image, lower=200, upper=500, insideValue=1, outsideValue=0)
cc = sitk.ConnectedComponent(binary)
stats = sitk.LabelIntensityStatisticsImageFilter()
stats.Execute(cc, image)

for label in stats.GetLabels():
    print(f"Label {label}: mean={stats.GetMean(label):.1f}")

Registration

fixed = sitk.ReadImage("template.nii.gz")
moving = sitk.ReadImage("moving.nii.gz")

R = sitk.ImageRegistrationMethod()
R.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
R.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100)
R.SetInitialTransform(sitk.CenteredTransformInitializer(fixed, moving, sitk.Euler3DTransform()))
final_transform = R.Execute(fixed, moving)
resampled = sitk.Resample(moving, fixed, final_transform, sitk.sitkLinear)

Workflow

1. Read images with sitk.ReadImage() (auto-detects format)
2. Preprocess: BinaryThreshold, MedianFilter, ResampleImageFilter
3. Segment with thresholding, watershed, or connected components
4. Register with ImageRegistrationMethod + transform
5. Measure volumes with LabelStatisticsImageFilter
6. Write results with sitk.WriteImage()