Chapter title |
Self-Aligning Manifolds for Matching Disparate Medical Image Datasets.
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Chapter number | 28 |
Book title |
Information Processing in Medical Imaging
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Published in |
Information processing in medical imaging proceedings of the conference, January 2015
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DOI | 10.1007/978-3-319-19992-4_28 |
Pubmed ID | |
Book ISBNs |
978-3-31-919991-7, 978-3-31-919992-4
|
Authors |
Baumgartner, Christian F, Gomez, Alberto, Koch, Lisa M, Housden, James R, Kolbitsch, Christoph, McClelland, Jamie R, Rueckert, Daniel, King, Andy P, Christian F. Baumgartner, Alberto Gomez, Lisa M. Koch, James R. Housden, Christoph Kolbitsch, Jamie R. McClelland, Daniel Rueckert, Andy P. King, Baumgartner, Christian F., Koch, Lisa M., Housden, James R., McClelland, Jamie R., King, Andy P. |
Abstract |
Manifold alignment can be used to reduce the dimensionality of multiple medical image datasets into a single globally consistent low-dimensional space. This may be desirable in a wide variety of problems, from fusion of different imaging modalities for Alzheimer's disease classification to 4DMR reconstruction from 2D MR slices. Unfortunately, most existing manifold alignment techniques require either a set of prior correspondences or comparability between the datasets in high-dimensional space, which is often not possible. We propose a novel technique for the 'self-alignment' of manifolds (SAM) from multiple dissimilar imaging datasets without prior correspondences or inter-dataset image comparisons. We quantitatively evaluate the method on 4DMR reconstruction from realistic, synthetic sagittal 2D MR slices from 6 volunteers and real data from 4 volunteers. Additionally, we demonstrate the technique for the compounding of two free breathing 3D ultrasound views from one volunteer. The proposed method performs significantly better for 4DMR reconstruction than state-of-the-art image-based techniques. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 3% |
Unknown | 30 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 29% |
Student > Ph. D. Student | 8 | 26% |
Student > Master | 3 | 10% |
Student > Doctoral Student | 2 | 6% |
Lecturer > Senior Lecturer | 2 | 6% |
Other | 4 | 13% |
Unknown | 3 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 9 | 29% |
Physics and Astronomy | 7 | 23% |
Medicine and Dentistry | 5 | 16% |
Engineering | 5 | 16% |
Unknown | 5 | 16% |