Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI

Kuldeep Kumar 1, 2 Laurent Chauvin 1 Mathew Toews 1 Olivier Colliot 2 Christian Desrosiers 1
2 ARAMIS - Algorithms, models and methods for images and signals of the human brain
UPMC - Université Pierre et Marie Curie - Paris 6, ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute, Inria de Paris
Abstract : So far, fingerprinting studies have focused on identifying features from single-modality MRI data, which capture individual characteristics in terms of brain structure, function, or white matter microstruc-ture. However, due to the lack of a framework for comparing across multiple modalities, studies based on multi-modal data remain elusive. This paper presents a multi-modal analysis of genetically-related subjects to compare and contrast the information provided by various MRI modalities. The proposed framework represents MRI scans as bags of SIFT features, and uses these features in a nearest-neighbor graph to measure subject similarity. Experiments using the T1/T2-weighted MRI and diffusion MRI data of 861 Human Connectome Project subjects demonstrate strong links between the proposed similarity measure and genetic proximity.
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Soumis le : lundi 18 septembre 2017 - 18:22:28
Dernière modification le : lundi 28 mai 2018 - 16:16:44

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Kuldeep Kumar, Laurent Chauvin, Mathew Toews, Olivier Colliot, Christian Desrosiers. Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI. Workshop on Computational Diffusion MRI, CDMRI 2017, MICCAI Workshop, Sep 2017, Quebec, Canada. 2017. 〈hal-01589647〉

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