tiny-metaio 0.3.0

Read and write MetaImages with minimal dependencies

MetaIO

Read MetaImages (.mha or .mhd) and NIFTI images (.nii or .nii.gz) and write .mha images in python with minimal dependencies.

Bonus: provides a custom 8-bit MHA-like format when size matters: .metaq.

Installation

Available on pypi.org: pip install tiny-metaio.

Usage

Writing a MHA file

>>> import metaio
>>> img = metaio.MetaImage([[0, 1], [42, 43]], spacing=[1, 2])
>>> img.save("some-name.mha")

Reading a MHA file

>>> img = metaio.read("some-name.mha")
>>> img.spacing
array([1., 2.])
>>> img.data
array([[ 0,  1],
       [42, 43]])
>>> img.direction
array([1., 0., 0., 1.])
>>> img.origin
array([0., 0.])

Custom format

You need to specify a range of values covered by the quantization. Values outside this ranges will be clipped. A loss of precision is expected. NaNs will be implicitly converted to zero.

>>> img = metaio.MetaImage([[-0.5, 1], [2.5, 5]])
>>> img.save_quantized("some-name.metaq", min_=0, max_=2.5)
>>> metaio.read("some-name.metaq").data
array([[0. , 1. ],
       [2.5, 2.5]], dtype=float32)

Additionally, if some values do not matter, you can specify a mask for further compression. The mask must be a binary array of the same shape as the image. Values where the mask is False will not be stored at all and restored as NaN on dequantization.

>>> img = metaio.MetaImage([[-0.5, 1], [2.5, 5]])
>>> img.save_quantized("some-name.metaq", min_=0, max_=2.5, mask=img.data <= 2.5)
>>> metaio.read("some-name.metaq").data
array([[0. , 1. ],
       [2.5, nan]], dtype=float32)

Command-line interface

A command-line interface is available to convert supported input format to a MHA.

$ metaio /path/to/input.metaq /path/to/output.mha

Philosophy

• numpy as only runtime dependency.
• idiomatic python.
• similar behavior than SimpleITK's GetArrayFromImage, GetImageFromArray, GetDirection, GetOrigin, GetSpacing, ReadImage, WriteImage.

Why should I use this over SimpleITK?

If you just need the IO parts and do not want the large SimpleITK package, or if you need to efficiently store some large MetaImage-like data, this package might be for you.

If you do not care about having SimpleITK as a dependency for your project, this package is not for you.