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Robust Registration of Multi-modal Medical Images Using Huber’s Criterion

Abstract : Registration of multi-modal medical images is an essential pre-processing step, for example, for fusion or image guided-interventions. However, the alignment process is prone to high variability in tissue appearance between modalities, in addition to local intensity variations and artefacts. This work introduces a robust multi-modal registration approach that mitigates the undesirable effect of such variability. Robustness is achieved using Huber's loss function for the data fidelity and regularization terms. We propose a novel approach using Huber's criterion, which enables a jointly convex estimation of the motions and the associated scale parameters. We formulate the problem as a complex 2D transformation estimation and investigate a robust total-variation smoothing, as well as a dictionary learning-based data fidelity term. Experiments are conducted using two datasets of multi-contrast MR brain images.
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Submitted on : Wednesday, February 3, 2021 - 2:17:28 PM
Last modification on : Monday, December 13, 2021 - 9:17:20 AM


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Nora Leïla Ouzir, Esa Ollila, Sergiy Vorobyov. Robust Registration of Multi-modal Medical Images Using Huber’s Criterion. Asilomar Conference on Signals, Systems, and Computers, Oct 2020, Pacific Grove, United States. ⟨10.1109/IEEECONF51394.2020.9443321⟩. ⟨hal-03130227⟩



Les métriques sont temporairement indisponibles