no code implementations • 25 Aug 2022 • Laurent Chauvin
A novel exponential kernel is proposed to quantify the similarity of a pair of features extracted in different images from their properties including location, scale, orientation, sign and appearance.
no code implementations • 30 May 2022 • Laurent Chauvin, William Wells III, Matthew Toews
Augmenting local feature properties with sign in addition to standard (location, scale, orientation) geometry leads to descriptors that are invariant to coordinate reflections and intensity contrast inversion.
1 code implementation • 19 Dec 2021 • Jean-Baptiste Carluer, Laurent Chauvin, Jie Luo, William M. Wells III, Ines Machado, Rola Harmouche, Matthew Toews
This work details a highly efficient implementation of the 3D scale-invariant feature transform (SIFT) algorithm, for the purpose of machine learning from large sets of volumetric medical image data.
no code implementations • 7 Oct 2021 • Laurent Chauvin, Matthew Toews
Subject ID labels are unique, anonymized codes that can be used to group all images of a subject while maintaining anonymity.
1 code implementation • 11 Mar 2021 • Laurent Chauvin, Kuldeep Kumar, Christian Desrosiers, William Wells III, Matthew Toews
Our measure generalizes the Jaccard index to account for soft set equivalence (SSE) between keypoint elements, via an adaptive kernel framework modeling uncertainty in keypoint appearance and geometry.
no code implementations • MIDL 2019 • Laurent Chauvin, Matthew Toews
We present an image keypoint-based morphological signature that can be used to efficiently assess the pair-wise whole-brain similarity for large MRI datasets.
no code implementations • 18 Sep 2017 • Kuldeep Kumar, Laurent Chauvin, Mathew Toews, Olivier Colliot, Christian Desrosiers
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 microstructure.