Conference Paper

Early detection of rejection in cardiac MRI: a spectral graph approach

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA
DOI: 10.1109/ISBI.2006.1624865 Conference: Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 6-9 April 2006
Source: IEEE Xplore

ABSTRACT This paper develops an algorithm to detect abnormalities of small animals' transplanted hearts in MRI, at early stage of rejection when the hearts do not display prominent abnormal features. Existing detection methods require experts to manually identify these abnormal regions. This task is time consuming, and the detection criteria are operator dependent. We present a semi-automatic approach that needs experts to label only a small portion of the motion maps. Our algorithm begins with representing the left ventricular motions by a weighted graph that approximates the manifold where these motions lie. We compute the eigendecomposition of the Laplacian of the graph and use these as basis functions to represent the classifier. The experimental results with synthetic data and real cardiac MRI data demonstrate the application of our classifier to early detection of heart rejection

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    • "In [2], we proposed a computer assisted method to detect regional heart malfunction based on spectral graph theory [3] [4] [5]. The classifier in [2] is semisupervised—initially, a human expert labels as normal or abnormal a small portion of the motions and then the classifier propagates the human prior knowledge to the remaining unlabeled motions. In early stages of heart disease, the abnormal motions are not prominent . "
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