Abstract
Pathology can have an important impact on MRI analysis. Specifically, white matter hyper-intensities, tumours, infarcts, etc., can influence the results of various image analysis techniques such as segmentation and registration. Several algorithms have been proposed for image inpainting and restoration, mainly in the context of Multiple Sclerosis lesions. These techniques commonly rely on a set of manually segmented pathological regions for inpainting. Rather than relying on prior segmentations for image restoration, we present a combined segmentation and inpainting algorithm for multimodal images. The proposed method is based on an iterative collaboration between two patch-based techniques, PatchMatch and Non-Local Means, where the former is used to estimate the most probable location of the pathological outliers and the latter to gradually fill the segmented areas with the most plausible multimodal texture. We demonstrate that the proposed method is able to automatically restore multimodal intensities in pathological regions within the context of Multiple Sclerosis.
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Acknowledgments
FP, BK and SO are funded by the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative-BW.mn.BRC10269). SO receives funding from the EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), the MRC (MR/J01107X/1) and the NIHR Biomedical Research Unit (Dementia) at UCL. This work was also supported by the Medical Research Council, the UK Multiple Sclerosis Society (grant 892/08) and the Brain Research Trust.
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Prados, F. et al. (2016). Fully Automated Patch-Based Image Restoration: Application to Pathology Inpainting. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_1
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