Hua-Mei Chen

General Electric, Fairfield, California, United States

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Publications (3)3.09 Total impact

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    ABSTRACT: This study investigates fast detection of the left ventricle (LV) endo- and epicardium boundaries in a cardiac magnetic resonance (MR) sequence following the optimization of two original discrete cost functions, each containing global intensity and geometry constraints based on the Bhattacharyya similarity. The cost functions and the corresponding max-flow optimization built upon an original bound of the Bhattacharyya measure yield competitive results in nearly real-time. Within each frame, the algorithm seeks the LV cavity and myocardium regions consistent with subject-specific model distributions learned from the first frame in the sequence. Based on global rather than pixel-wise information, the proposed formulation relaxes the need of a large training set and optimization with respect to geometric transformations. Different from related active contour methods, it does not require a large number of iterative updates of the segmentation and the corresponding computationally onerous kernel density estimates (KDEs). The algorithm requires very few iterations and KDEs to converge. Furthermore, the proposed bound can be used for several other applications and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of max-flow optimization. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert. Moreover, comparisons with a related recent active contour method showed that the proposed framework brings significant improvements in regard to accuracy and computational efficiency.
    Medical image analysis 05/2011; 16(1):87-100. · 3.09 Impact Factor
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    ABSTRACT: This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of an auxiliary function by graph cuts. Then, we demonstrate that the proposed procedure converges and give a statistical interpretation of the upper bound. The algorithm requires very few iterations to converge, and finds nearly global optima. Quantitative evaluations and comparisons with state-of-the-art methods on the Microsoft GrabCut segmentation database demonstrated that the proposed algorithm brings improvements in regard to segmentation accuracy, computational efficiency, and optimality. We further demonstrate the flexibility of the algorithm in object tracking.
    The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13-18 June 2010; 01/2010
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    ABSTRACT: This study investigates a new parameterization of deformation fields for image registration. Instead of standard displacements, this parameterization describes a deformation field with its transformation Jacobian and curl of end velocity field. It has two important features which make it appealing to image registration: 1) it relaxes the need of an explicit regularization term and the corresponding ad hoc weight in the cost functional; 2) explicit constraints on transformation Jacobian such as topology preserving and incompressibility constraints are straightforward to impose in a unified framework. In addition, this parameterization naturally describes a deformation field in terms of radial and rotational components, making it especially suited for processing cardiac data. We formulate diffeomorphic image registration as a constrained optimization problem which we solve with a step-then-correct strategy. The effectiveness of the algorithm is demonstrated with several examples and a comprehensive evaluation of myocardial delineation over 120 short-axis cardiac cine MRIs acquired from 20 subjects. It shows competitive performance in comparison to two recent segmentation based approaches.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 1):340-8.