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Found 7 papers, 2 papers with code
Using Atom-Like Local Image Features to Study Human Genetics and Neuroanatomy in Large Sets of 3D Medical Image Volumes
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.
Registering Image Volumes using 3D SIFT and Discrete SP-Symmetry
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.
GPU optimization of the 3D Scale-invariant Feature Transform Algorithm and a Novel BRIEF-inspired 3D Fast Descriptor
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.
Curating Subject ID Labels using Keypoint Signatures
Subject ID labels are unique, anonymized codes that can be used to group all images of a subject while maintaining anonymity.
Efficient Pairwise Neuroimage Analysis using the Soft Jaccard Index and 3D Keypoint Sets
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.
A Keypoint-based Morphological Signature for Large-scale Neuroimage Analysis
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.
Multi-modal analysis of genetically-related subjects using SIFT descriptors in brain MRI
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.
