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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