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Normalizes medical image intensities using various methods (z-score, min-max, percentile, window-level). Essential preprocessing step for consistent analysis across different scanners and protocols.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Scan medical imaging directories and generate structured JSON or CSV manifests with file-level metadata for DICOM and NIfTI assets.
Validates DICOM series consistency by checking SeriesInstanceUID, ImageOrientationPatient, slice spacing uniformity, and detecting missing slices.
Generate research-oriented HTML or Markdown case reports for volumetric medical images, including summary statistics and slice montages.
Resample NIfTI medical images to target spacing with configurable interpolation methods and optional anti-aliasing for downsampling.
Generates a complete MONAI-based training scaffold for medical image segmentation or classification tasks. Creates training scripts, inference scripts, configuration files, data loading pipelines, and model definitions.
Validates NIfTI file headers for integrity and consistency. Checks header structure, dimension consistency, orientation matrix, data type support, and file size expectations.
| name | intensity-normalizer |
| description | Normalizes medical image intensities using various methods (z-score, min-max, percentile, window-level). Essential preprocessing step for consistent analysis across different scanners and protocols. |
| version | 0.1.0 |
| author | Medical AI Team <team@example.com> |
| license | MIT |
| tags | ["preprocessing","intensity","normalization","nifti","dicom"] |
| maturity | stable |
| category | preprocessing |
| requires_extras | ["core"] |
This skill normalizes medical image intensities using multiple standard methods. Intensity normalization is critical for medical image analysis because different scanners, protocols, and acquisition parameters can produce vastly different intensity ranges even for the same tissue type.
Use this skill when you need to:
pip install -r requirements.txt
Dependencies:
./scripts/main.py [OPTIONS] INPUT_PATH
| Argument | Required | Description |
|---|---|---|
input_path | Yes | Path to input image file (NIfTI, NRRD, or other supported formats) |
| Option | Default | Description |
|---|---|---|
--output, -o | stdout | Output file path for normalized image |
--method | z-score | Normalization method: z-score, min-max, percentile, window-level |
--percentile-low | 1 | Lower percentile for percentile method (0-100) |
--percentile-high | 99 | Upper percentile for percentile method (0-100) |
--window-center | 0 | Window center for window-level method (HU units) |
--window-width | 400 | Window width for window-level method (HU units) |
--exclude-background | false | Exclude background (zero) values from normalization |
--verbose, -v | false | Enable verbose logging |
--version | - | Show version and exit |
--help | - | Show help message |
Takes a file path to a medical image file. Supported formats include:
The image should contain numeric intensity values (typically int16, int32, float32, or float64).
{
"success": true,
"result": {
"input_path": "string - original input path",
"output_path": "string - path to normalized image",
"method": "string - normalization method used",
"input_stats": {
"mean": "float - mean intensity before normalization",
"std": "float - standard deviation before normalization",
"min": "float - minimum intensity",
"max": "float - maximum intensity",
"nonzero_count": "integer - number of non-zero voxels"
},
"output_stats": {
"mean": "float - mean intensity after normalization",
"std": "float - standard deviation after normalization",
"min": "float - minimum intensity",
"max": "float - maximum intensity"
},
"parameters": {
"method-specific parameters used"
}
},
"metadata": {
"timestamp": "ISO 8601 timestamp",
"duration_ms": "integer - processing time",
"version": "string - skill version"
}
}
| Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Validation error (invalid method, bad parameters) |
| 2 | Runtime error (file not found, read/write error) |
| 3 | Usage error (missing required arguments) |
./scripts/main.py /path/to/image.nii.gz --output normalized.nii.gz
Output:
{
"success": true,
"result": {
"input_path": "/path/to/image.nii.gz",
"output_path": "normalized.nii.gz",
"method": "z-score",
"input_stats": {
"mean": 1234.5,
"std": 456.7,
"min": 0,
"max": 4095,
"nonzero_count": 125000
},
"output_stats": {
"mean": 0.0,
"std": 1.0,
"min": -2.69,
"max": 6.26
}
}
}
./scripts/main.py /path/to/image.nii.gz --output normalized.nii.gz --method min-max
./scripts/main.py /path/to/image.nii.gz -o normalized.nii.gz \
--method percentile \
--percentile-low 0.5 \
--percentile-high 99.5 \
--exclude-background
./scripts/main.py /path/to/ct_scan.nii.gz -o lung_window.nii.gz \
--method window-level \
--window-center -600 \
--window-width 1500
./scripts/main.py /path/to/image.nii.gz -o normalized.nii.gz --verbose
Stderr output:
Input file: /path/to/image.nii.gz
Method: z-score
Input shape: (256, 256, 128)
Input stats: mean=1234.5, std=456.7, min=0, max=4095
Output written to: normalized.nii.gz
Processing completed in 245ms
Transforms intensities to have zero mean and unit variance:
x_normalized = (x - mean) / std
Best for: General purpose normalization, machine learning inputs.
Scales intensities to [0, 1] range:
x_normalized = (x - min) / (max - min)
Best for: Visualization, algorithms expecting [0, 1] range.
Clips values to specified percentiles, then scales to [0, 1]:
x_clipped = clip(x, percentile_low, percentile_high)
x_normalized = (x_clipped - p_low) / (p_high - p_low)
Best for: Robust normalization, handling outliers.
Applies CT window/level transformation:
x_normalized = (x - (center - width/2)) / width
Values outside [0, 1] after transformation are clipped.
Best for: CT scan visualization and analysis with specific tissue windows.
For research use only. This skill is not intended for clinical diagnosis or treatment. Always validate outputs and comply with applicable regulations.