Manifold Learning for Medical Image Registration, Segmentation, and Classification

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Author(s): Paul Aljabar (Imperial College London, UK), Robin Wolz (Imperial College London, UK)and Daniel Rueckert (Imperial College London, UK)
Copyright: 2012
Pages: 22
Source title: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis
Source Author(s)/Editor(s): Kenji Suzuki (University of Chicago, USA)
DOI: 10.4018/978-1-4666-0059-1.ch017

Keywords: Computational Intelligence / Computer Science & IT / Medical Information Science Reference / Medical Technologies

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Abstract

The term manifold learning encompasses a class of machine learning techniques that convert data from a high to lower dimensional representation while respecting the intrinsic geometry of the data. The intuition underlying the use of manifold learning in the context of image analysis is that, while each image may be viewed as a single point in a very high-dimensional space, a set of such points for a population of images may be well represented by a sub-manifold of the space that is likely to be non-linear and of a significantly lower dimension. Recently, manifold learning techniques have begun to be applied to the field of medical image analysis. This chapter will review the most popular manifold learning techniques such as Multi-Dimensional Scaling (MDS), Isomap, Local linear embedding, and Laplacian eigenmaps. It will also demonstrate how these techniques can be used for image registration, segmentation, and biomarker discovery from medical images.

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Manifold Learning for Medical Image Registration, Segmentation, and Classification

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