Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images

27 March 2009 Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images

Konstantin Ens, Fabian Wenzel, Stewart Young, Jan Modersitzki, Bernd Fischer

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Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725933 (2009) https://doi.org/10.1117/12.811320
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Image registration and segmentation are two important tasks in medical image analysis. However, the validation of algorithms for non-linear registration in particular often poses significant challenges:1, 2 Anatomical labeling based on scans for the validation of segmentation algorithms is often not available, and is tedious to obtain. One possibility to obtain suitable ground truth is to use anatomically labelled atlas images. Such atlas images are, however, generally limited to single subjects, and the displacement field of the registration between the template and an arbitrary data set is unknown. Therefore, the precise registration error cannot be determined, and approximations of a performance measure like the consistency error must be adapted. Thus, validation requires that some form of ground truth is available. In this work, an approach to generate a synthetic ground truth database for the validation of image registration and segmentation is proposed. Its application is illustrated using the example of the validation of a registration procedure, using 50 magnetic resonance images from different patients and two atlases. Three different non-linear image registration methods were tested to obtain a synthetic validation database consisting of 50 anatomically labelled brain scans.

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Konstantin Ens, Fabian Wenzel, Stewart Young, Jan Modersitzki, and Bernd Fischer "Design of a synthetic database for the validation of non-linear registration and segmentation of magnetic resonance brain images", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725933 (27 March 2009); https://doi.org/10.1117/12.811320

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