Chapter title |
Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex
|
---|---|
Chapter number | 3 |
Book title |
Information Processing in Medical Imaging
|
Published in |
Information processing in medical imaging proceedings of the conference, June 2017
|
DOI | 10.1007/978-3-319-59050-9_3 |
Pubmed ID | |
Book ISBNs |
978-3-31-959049-3, 978-3-31-959050-9
|
Authors |
Suyash P. Awate, Richard M. Leahy, Anand A. Joshi |
Abstract |
Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a Riemannian manifold. We propose new regularized nonlinear methods for (i) detecting group differences, using a Mercer kernel with an implicit lifting map to a reproducing kernel Hilbert space, and (ii) regression against clinical variables, using kernel density estimation. For both methods, we employ kernels that exploit the Riemannian structure. Results on simulated and clinical data shows the improved accuracy and stability of our approach in cortical-sheet analysis. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 9 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 22% |
Researcher | 2 | 22% |
Student > Master | 2 | 22% |
Student > Postgraduate | 1 | 11% |
Unknown | 2 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 2 | 22% |
Medicine and Dentistry | 2 | 22% |
Computer Science | 1 | 11% |
Unknown | 4 | 44% |