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SimpletITK: Advanced Image Analysis for Python

Summary

SimpleITK brings advanced image analysis capabilities to Python. In particular, it provides support for 2D/3D and multi-components images with physical. SimpleITK exposes a large collection of image processing filters from ITK, including image segmentation and registration. SimpleITK is freely available as an open source package under the Apache 2.0 License.

Description

SimpleITK provides scientific image analysis, processing, segmentation and registration for biomedical, microscopy and other scientific fields by supporting multi-dimensional images with physical locations [1]. It's is a layer build upon the Insight Segmentation and Registration Toolkit (ITK) [2].

While there are many Python packages to process 2D photographic images, scientific image analysis adds additional requirements. Images encountered in these domains often have anisotropic pixel spacing, or spatial orientations, and calculations are best performed in physical space as opposed to pixel space.

SimpleITK brings to Python a plethora of capabilities for performing image analysis. Although SimpleITK was developed by the biomedical imaging community, it is also used for generic image processing. It differentiates from OpenCV in offering 3D images and multi-component images, and it differentiates from scipy by offering the abstraction of image classes and their associated data structures. This applies to images modalities such as CT scans, MRI, fMRI, ultrasound, and in microscopy modalities such as confocal, SEM, TEM, and traditional bright and dark field.

Among the key functionalities supported by SimpleITK are over 260 advanced image filtering and segmentation algorithms as well as access to scientific image file formats, including specialized formats such as DICOM, Nifti, NRRD, VTK and other formats that preserve 3D metadata. Example algorithms include Level Sets Segmentation including multi-phase, Label Maps, Region Growing, Statistical Classification, Advanced Thresholding, Geometrical Transformations, Deconvolution, Anti-Aliasing, Edge Detection, Mathematical Morphology on both labels and grayscale images and Fourier Analysis [4,5].

SimpleITK is an open source project with an active community, that builds upon the large amount of image analysis experience of the ITK community [3] working in biomedical images analysis since 1999, and that continues to grow year by year, aggregating state of the art algorithms .

SimpleITK development is sponsored by the US National Library of Medicine.

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