![]() ![]() ![]() Many software solutions have been developed for special purposes and applications, and there is a variety of toolkits, frameworks, and coding packages already available for medical image registration. They are essential but need clarity regarding pros and cons in features as well as in appropriateness for certain registration tasks. Freely available software fosters research in medical image analysis. However, none of these valuable sources refer to the accessibility of tools. Several textbooks also provide extensive discussion on the topic of medical image registration. , while others emphasize high-performance computing (HPC), metaheuristics such as evolutionary algorithms, and implementations on the graphical processing units. Image registration in the computational intelligence framework has been reviewed by Ramirez et al. ![]() Other authors have drawn attention to specific anatomic regions, for example, cardiac image registration, or on techniques used for specific purposes, such as brain functional image localization, optical breast imaging, minimally invasive surgical procedures, and coronary heart disease. Holden has reviewed non-rigid geometric transformation models. 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). We catalogue available software solutions for non-rigid image registration to support scientists in selecting suitable tools for specific medical registration purposes.
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