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Conference Papers Year : 2017

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

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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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Dates and versions

hal-01589647 , version 1 (18-09-2017)

Licence

Attribution - CC BY 4.0

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  • HAL Id : hal-01589647 , version 1

Cite

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. ⟨hal-01589647⟩
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