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Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets.
- Source :
-
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2020 Apr; Vol. 42 (4), pp. 988-997. Date of Electronic Publication: 2019 Jan 09. - Publication Year :
- 2020
-
Abstract
- Manifold alignment (MA) is a technique to map many high-dimensional datasets to one shared low-dimensional space. Here we develop a pipeline for using MA to reconstruct high-resolution medical images. We present two key contributions. First, we develop a novel MA scheme in which each high-dimensional dataset can be differently weighted preventing noisier or less informative data from corrupting the aligned embedding. We find that this generalisation improves performance in our experiments in both supervised and unsupervised MA problems. Second, we use the wave kernel signature as a graph descriptor for the unsupervised MA case finding that it significantly outperforms the current state-of-the-art methods and provides higher quality reconstructed magnetic resonance volumes than existing methods.
Details
- Language :
- English
- ISSN :
- 1939-3539
- Volume :
- 42
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- IEEE transactions on pattern analysis and machine intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 30629492
- Full Text :
- https://doi.org/10.1109/TPAMI.2019.2891600