Adaptive Curve Region based Motion Estimation and Motion Visualization of Cardiac Ultrasound Imaging
This paper presents a novel method for motion estimation and visualization of cardiac ultrasound images. The motion vectors are derived from an adaptive curve region based matching algorithm. The size of curve regions can be adjusted adaptively based on ultrasound system parameters and position information of the region's center which is located on the scan lines as sampling points. Then the Curve Minimum Mean of Absolute Difference (CMMAD) method is proposed to the curve region to estimate the motion vector field of two successive images. After obtained a vector field of the sampling points, we employ the Thin-Plate Spline (TPS) transformation function to recover the non-rigid motion and interpolation motion vectors for each pixel in the region of interest (ROI) of the ultrasound image. Finally we apply the Unsteady Flow Line Integral Convolution (UFLIC) to the vector field for motion visualization. Speckle tracking is one of methods for motion estimation of ultrasound images which is commonly implemented by block- matching method. A series of block-matching algorithms for motion estimation have been proposed in recent years, and most of them are based on rectangular blocks. These methods become less effective when they are applied to cardiac ultrasound video images acquired by a phase array probe. In this paper, a Curve Minimum Mean of Absolute Difference (CMMAD) method is presented for motion estimation using adaptive curve regions as criteria. After the displacement of each sampling points is extracted from two successive images, we introduced Thin-Plate Spline (TPS) interpolation algorithm (2) to recover the motion of heart structure and reconstruct the local motion field. TPS is a basis functions based interpolation method and is widely used in non-rigid image registration and shape matching (3). Visualization of vector fields is a challenging issue in scientific visualization and computer graphics. The best known is perhaps Line Integral Convolution (LIC) technique developed by Cabral and Leedom (4). The basic idea of the LIC is displaying a texture where each output pixel value is computed by convolving a white noise texture along the streamline of the vector field in both directions from the pixel location. The output of LIC algorithm provides a global dense representation of the flow. However, LIC is computed following the traces of streamline in a steady flow field, which cannot be readily used for visualizing unsteady flow data. Forssell and Cohen (5) proposed an extension by modifying the convolution path from streamlines to pathlines for visualizing unsteady vector fields. However, they reported that flow lines in the output images become ambiguous when the convolution window is set too wide. Shen and Kao (6) proposed a new algorithm for visualizing unsteady flow which is called Unsteady Flow Line Integral Convolution (UFLIC). UFLIC uses a time-accurate value scattering scheme and a successive texture feed-forward approach to achieve high temporal and spatial coherence. In this paper, we use the UFLIC method to visualize the time-varying vector fields.