Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Image distance functions for manifold learning
Image and Vision Computing
Patient position detection for SAR optimization in magnetic resonance imaging
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Manifold learning for image-based breathing gating with application to 4D ultrasound
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Sparse projections of medical images onto manifolds
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Magnetic resonance imaging is performed without ionizing radiation, however, the applied radio frequency power leads to heating, which is dependent on the body part being imaged. Determining the patient position in the scanner allows to better monitor the absorbed power and therefore optimize the image acquisition. Low-resolution images, acquired during the initial placement of the patient in the scanner, are exploited for detecting the patient position. We use Laplacian eigenmaps, a manifold learning technique, to learn the low-dimensional manifold embedded in the high-dimensional image space. Our experiments clearly show that the presumption of the slices lying on a low dimensional manifold is justified and that the proposed integration of neighborhood slices and image normalization improves the method. We obtain very good classification results with a nearest neighbor classifier operating on the low-dimensional embedding.