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Publications (2)2.57 Total impact

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    ABSTRACT: To automatically and robustly detect the arterial input function (AIF) with high detection accuracy and low computational cost in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, we developed an automatic AIF detection method using an accelerated version (Fast-AP) of affinity propagation (AP) clustering. The validity of this Fast-AP-based method was proved on two DCE-MRI datasets, i.e., rat kidney and human head and neck. The detailed AIF detection performance of this proposed method was assessed in comparison with other clustering-based methods, namely original AP and K-means, as well as the manual AIF detection method. Both the automatic AP- and Fast-AP-based methods achieved satisfactory AIF detection accuracy, but the computational cost of Fast-AP could be reduced by 64.37-92.10% on rat dataset and 73.18-90.18% on human dataset compared with the cost of AP. The K-means yielded the lowest computational cost, but resulted in the lowest AIF detection accuracy. The experimental results demonstrated that both the AP- and Fast-AP-based methods were insensitive to the initialization of cluster centers, and had superior robustness compared with K-means method. The Fast-AP-based method enables automatic AIF detection with high accuracy and efficiency. J. Magn. Reson. Imaging 2013. © 2013 Wiley Periodicals, Inc.
    Journal of Magnetic Resonance Imaging 10/2013; · 2.57 Impact Factor
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    ABSTRACT: In this paper, an automatic method to segment the blood vessel for 3D MRA (Magnetic Resonance Angiography) is presented. The segmentation process classifies MRA data into two parts: background and blood vessels. The process includes statistical model based on the voxel intensity and MRF model based on the context information of voxels. Both the models were built on 3D voxel, rather than on 2D. The proposed method is tested on the 3D Time-Of-Flight (TOF) -MRA data. The segmentation results give a good performance in extracting blood vessels.
    International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, China, July 10-13, 2011, Proceedings; 01/2011