مؤتمر
Robust Registration of Multi-modal Medical Images Using Huber’s Criterion
| العنوان: | Robust Registration of Multi-modal Medical Images Using Huber’s Criterion |
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| المؤلفون: | Ouzir, Nora Leïla, Ollila, Esa, Vorobyov, Sergiy |
| المساهمون: | Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay, School of Electrical Engineering Aalto Univ, Aalto University |
| المصدر: | Asilomar Conference on Signals, Systems, and Computers ; https://inria.hal.science/hal-03130227Test ; Asilomar Conference on Signals, Systems, and Computers, Oct 2020, Pacific Grove, United States. ⟨10.1109/IEEECONF51394.2020.9443321⟩ |
| بيانات النشر: | HAL CCSD |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Multi-modal registration, robust registration, Huber's criterion, coupled dictionary learning, MR-T1, MR-T2, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging |
| جغرافية الموضوع: | Pacific Grove, United States |
| الوصف: | International audience ; 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. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| العلاقة: | hal-03130227; https://inria.hal.science/hal-03130227Test; https://inria.hal.science/hal-03130227/documentTest; https://inria.hal.science/hal-03130227/file/paper_ASILOMAR_vf.pdfTest |
| DOI: | 10.1109/IEEECONF51394.2020.9443321 |
| الإتاحة: | https://doi.org/10.1109/IEEECONF51394.2020.9443321Test https://inria.hal.science/hal-03130227Test https://inria.hal.science/hal-03130227/documentTest https://inria.hal.science/hal-03130227/file/paper_ASILOMAR_vf.pdfTest |
| حقوق: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.94BCB1E7 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1109/IEEECONF51394.2020.9443321 |
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