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A robust and extendible framework for medical image registration focused on rapid clinical application deployment

2011; Elsevier BV; Volume: 41; Issue: 6 Linguagem: Inglês

10.1016/j.compbiomed.2011.03.011

1879-0534

Tobias Boehler, D. van Straaten, Stefan Wirtz, Heinz‐Otto Peitgen,

Radiomics and Machine Learning in Medical Imaging

Development and integration of image registration methods become increasingly important for clinical workstations. Due to the complexity of such methods, prototyping, evaluation and workflow integration require in-depth knowledge foremostly available to registration developers. Rapid development and deployment is therefore often difficult, particularly for comprehensive software frameworks. In this article, we introduce a novel rapid prototyping framework for voxel-based registration. It is specifically designed to allow evaluation and adaption by developers with less knowledge on registration internals. Based on a unique "one-iteration" paradigm, it enables accelerated algorithm development. Furthermore, methods are interchangeable at runtime using an intuitive graphical plugin interface, allowing a genuine comparison of methods. We discuss the concepts of this framework, compare it to other software and demonstrate its effectiveness for several demanding registration applications, highlighting its versatility and reliability.