itk

Multi-platform, free open source software for visualization and image computing.

Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.

SimpleITK: a layer built on top of the Insight Toolkit (ITK), intended to simplify and facilitate ITK's use in rapid prototyping, education and interpreted languages.

A set of common support code for medical imaging, surgical navigation, and related purposes.

⚠ OBSOLETE | Multi-platform, free open source software for visualization and image computing.

An elegant Python interface for visualization on the web platform to interactively generate insights into multidimensional images, point sets, and geometry.

Medical image processing in Python

Curvature Filters are efficient solvers for Variational Models

An ITK Python interface to elastix, a toolbox for rigid and nonrigid registration of images

High performance spatial analysis in a web browser and across programming languages and hardware architectures

Python script to extract a STL surface from a DICOM image series.

Starviewer, a cross-platform open source medical imaging software

Library and executables for modeling and registration applications in medical image analysis. Particular emphasis on intraoperative fluoroscopic (X-ray) navigation via 2D/3D registration.

Surgical Image Guidance and Healthcare Toolkit

A setup script to generate ITK Python Wheels

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.

Template to be used as a starting point for creating a custom 3D Slicer application

ITK module with classes particularly useful for ultrasound.

The Medical Image Segmentation Tool Set (iSEG) is a fully integrated segmentation (including pre- and postprocessing) toolbox for the efficient, fast, and flexible generation of anatomical models from various types of imaging data

A Tcl/Tk Frontend for GDB. This is an AppImage(Portable Package) of insight for the sake of Jeff Duntemann's amazing book.