NDLI: Diffusion Map Approach to Classifying Early Stage Cardiac Dysfunction

Content Provider	IEEE Xplore Digital Library
Author	Chang, Hsun-Hsien Moura, Jose M. F. Wu, Yijen L. Ho, Chien
Copyright Year	2007
Description	Author affiliation: Pittsburgh NMR Center for Biomedical Research, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, USA (Ho, Chien) \|\| Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, PA, USA (Wu, Yijen L.) \|\| Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. moura@cmu.edu (Moura, Jose M. F.) \|\| Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. hsunhsien@cmu.edu (Chang, Hsun-Hsien)
Abstract	Magnetic resonance (MR) tagging technology can assist us in determining the motions of the myocardial pixels in a sequence of MR images. This paper presents a semi-supervised algorithm that processes these motion maps and classifies automatically myocardial dysfunctional motions. In distinction with other methods, our algorithm requires that only a few normal motions are labeled a priori. This is significant because, while normal motions can be confidently labeled by a human expert, abnormal motions are very difficult to label with high reliability by an operator. We use a graph to capture the motion map of the left ventricle. The normalized weighted adjacency matrix of the graph is interpreted as a stochastic matrix. Performing random walks, or diffusion, on the graph determines how similar myocardial motions are. Similar motions on the graph are represented by the diffusion maps framework as closer vectors in a Euclidean space. In the Euclidean space, we adopt eigen-analysis on a small portion of labeled normal motions. The analysis leads to a hyperelliptic surface that classifies the remaining cardiac motions as normal or dysfunctional.
Starting Page	615
Ending Page	619
File Size	529530
Page Count	5
File Format	PDF
ISBN	9781424411979
DOI	10.1109/SSP.2007.4301332
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Myocardium Humans Space technology Biology computing Biomedical computing Nuclear magnetic resonance Graph theory Labeling Stochastic processes Magnetic resonance spectral graph cardiac motion dysfunction classification diffusion maps
Content Type	Text
Resource Type	Article

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