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- Title
Comparative analysis of nonlinear dimensionality reduction techniques for breast MRI segmentation.
- Authors
Akhbardeh, Alireza; Jacobs, Michael A
- Abstract
Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis.
- Publication
Medical physics, 2012, Vol 39, Issue 4, p2275
- ISSN
0094-2405
- Publication type
Journal Article
- DOI
10.1118/1.3682173