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SICLE / Small-deformation Inverse-Consistent Linear-Elastic image Registration

Allows users to estimate the forward and reverse transformation between two images. SICLE is an image registration algorithm that determines the forward and reverse transformation between them while minimizing the inverse consistency error. It acts like that to cut the correspondence between these transformations associated with large inverse consistency errors. The estimated transformations are regularized thanks to a thin-plate spline (TPS) model.

AMIDE / Amide's a Medical Imaging Data Examiner

Displays and analyzes multimodality volumetric medical images. AMIDE provides the research community with a relatively full-featured, freely available, and open source solution for single and multimodality volumetric medical image analysis. It provides a variety of additional features useful to the molecular imaging researcher, including fully three dimensional ROI drawing and analysis for static and dynamic images, two and three way linked viewing (dual cursor mode), rigid body registration using fiducial markers, filtering and cropping of data sets, movie generation, series viewing, and volume rendering.

ITK / Insight Segmentation and Registration Toolkit

Allows to perform registration and segmentation for image analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. ITK uses a model of software development known as Extreme Programming. The sampled representation is an image acquired from such medical instrumentation as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or ultrasound scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.

Evaluation of similarity measures

Provides a protocol that enables a thorough, optimization-independent, and systematic statistical evaluation of important similarity measure properties. Evaluation of similarity measures includes Accuracy (ACC), Distinctiveness of the Optimum (DO), Capture Range (CR), Number of Local Minima (NOM), Risk of Non-convergence (RON). The evaluation consists of three steps: (i) sampling of the parametrical space, (ii) computation of similarity measure values and (iii) computation of similarity measure properties.

Mango / Multi-image Analysis GUI

Automates regional behavioral analysis of human brain images. Mango provides analysis tools and a user interface to navigate image volumes. The tool is ease to use, multi-platform Java application and extensive region of interest tools. It has the ability to add and update software as a plugin module and offers full access to a suite of image viewing and processing features. The software is able to rapidly determine regionally specific behaviors for researchers’ brain studies.