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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