The comprehensive review by Sotiras has focused on deformable medical image registration methods classifying methods on the basis of the core registration components: (i) deformation models, (ii) matching criteria, and (iii) optimization. have compared similarity measures used in registration methods. Hence, they provide a broad picture of the rapidly evolving registration methods. Indeed, most reviews focus on algorithms, modalities involved, and the characteristics of the registration task.
Medical image registration methods in general have been reviewed by several authors, most recently by Oliveira & Tavares. In the medical field, image registration is a key component in several areas including the fusion of morphologic and functional images image subtraction intervention planning computer-aided diagnosis (CAD) and treatment follow-up intervention simulations atlas building radiation therapy model-based segmentation and computational model building.
It is an important part of image analysis and used in several disciplines.
Image registration aims at finding the optimal transform that best aligns structures in two input (2D) or volume data (3D) images. However, the most recently published algorithms may not be included in the tools, yet. Researchers in medical image analysis already have a large choice of registration tools freely available. Based on open source, licensing, GPU support, active community, several file formats, algorithms, and similarity measures, the tools Elastics and Plastimatch are chosen for the platform ITK and without platform requirements, respectively.
Out of the 18 tools, (i) 12 are open source, 8 are released under a permissive free license, which imposes the least restrictions on the use and further development of the tool, 8 provide graphical processing unit (GPU) support (ii) 7 are built on software platforms, 5 were developed for brain image registration (iii) 6 are under active development but only 3 have had their last update in 2015 or 2016 (iv) 16 support the Analyze format, while 7 file formats can be read with only one of the tools and (v) 6 provide multiple registration methods and 6 provide landmark-based registration methods. The remaining ( n = 18) tools were classified by (i) access and technology, (ii) interfaces and application, (iii) living community, (iv) supported file formats, and (v) types of registration methodologies emphasizing the similarity measures implemented. Exclusions are due to unavailability or inappropriateness. Registration tools were identified using non-systematic search in Pubmed, Web of Science, IEEE Xplore® Digital Library, Google Scholar, and through references in identified sources ( n = 22). In that manner, once a researcher has identified the data he / she needs via an Internet search using meaningful descriptors, the data can be directly downloaded via standard DICOM query and retrieve utilities and that data will be perpetually indexed and accessible via an Internet handle.We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes.
Specifically, DICOM objects (and other medical image data formats such at Analyze,MetaImage) uploaded to a DADL can be grouped with other files such as journal articles or tracker data, viewed as thumbnails, and made publicly or privately available for others to search and download from a linked DICOM server. We have extended and linked existing, freely-available DICOM and digital library software to create DICOM Accessible Digital Libraries (DADLs). with a companion image access and archival system that merges the image transfer capabilities of DICOM with DSpace (an open-source web-based digital library technology. In this paper, we propose an extension to the National Library of Medicine's Insight Segmentation and Registration Toolkit (ITK.