10 March 2006 Cardiac motion estimation by using high-dimensional features and K-means clustering method
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Abstract
Tagged Magnetic Resonance Imaging (MRI) is currently the reference modality for myocardial motion and strain analysis. Mutual Information (MI) based non rigid registration has proven to be an accurate method to retrieve cardiac motion and overcome many drawbacks present on previous approaches. In a previous work1, we used Wavelet-based Attribute Vectors (WAVs) instead of pixel intensity to measure similarity between frames. Since the curse of dimensionality forbids the use of histograms to estimate MI of high dimensional features, k-Nearest Neighbors Graphs (kNNG) were applied to calculate α-MI. Results showed that cardiac motion estimation was feasible with that approach. In this paper, K-Means clustering method is applied to compute MI from the same set of WAVs. The proposed method was applied to four tagging MRI sequences, and the resulting displacements were compared with respect to manual measurements made by two observers. Results show that more accurate motion estimation is obtained with respect to the use of pixel intensity.
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Estanislao Oubel, Alfred O. Hero, Alejandro F. Frangi, "Cardiac motion estimation by using high-dimensional features and K-means clustering method", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614417 (10 March 2006); doi: 10.1117/12.653921; https://doi.org/10.1117/12.653921
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