# Simpleitk

> Simpleitk is an Agent Skill for documents published in the mkurman/zorai repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Simplified interface to the Insight Toolkit (ITK) for medical image processing. Segmentation, registration, filtering, resampling, morphological operations. Supports DICOM, NIfTI, NRRD, dozens of formats.

- Skill name: `simpleitk`
- Category: [Documents](https://www.openparable.com/skill/documents/1)
- Source: https://github.com/mkurman/zorai/blob/HEAD/skills/scientific-skills/simpleitk/SKILL.md
- Repository: mkurman/zorai (320 GitHub stars when collected)

## SKILL.md

## 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

```bash
uv pip install SimpleITK
```

## Basic Image Operations

```python
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

```python
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

```python
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()`

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Source: https://www.openparable.com/skill/documents/simpleitk (OpenParable). Cite the canonical URL above.