Mikić I, Krucinski S, Thomas J D
Department of Electrical Engineering, University of California, San Diego 92122, USA.
IEEE Trans Med Imaging. 1998 Apr;17(2):274-84. doi: 10.1109/42.700739.
This paper presents a method for segmentation and tracking of cardiac structures in ultrasound image sequences. The developed algorithm is based on the active contour framework. This approach requires initial placement of the contour close to the desired position in the image, usually an object outline. Best contour shape and position are then calculated, assuming that at this configuration a global energy function, associated with a contour, attains its minimum. Active contours can be used for tracking by selecting a solution from a previous frame as an initial position in a present frame. Such an approach, however, fails for large displacements of the object of interest. This paper presents a technique that incorporates the information on pixel velocities (optical flow) into the estimate of initial contour to enable tracking of fast-moving objects. The algorithm was tested on several ultrasound image sequences, each covering one complete cardiac cycle. The contour successfully tracked boundaries of mitral valve leaflets, aortic root and endocardial borders of the left ventricle. The algorithm-generated outlines were compared against manual tracings by expert physicians. The automated method resulted in contours that were within the boundaries of intraobserver variability.
本文提出了一种用于超声图像序列中心脏结构分割与跟踪的方法。所开发的算法基于主动轮廓框架。这种方法需要将轮廓初始放置在图像中接近期望位置处，通常是物体轮廓。然后计算最佳轮廓形状和位置，假设在此配置下与轮廓相关联的全局能量函数达到其最小值。通过从前一帧中选择一个解作为当前帧的初始位置，主动轮廓可用于跟踪。然而，对于感兴趣物体的大位移，这种方法会失效。本文提出了一种将像素速度信息（光流）纳入初始轮廓估计以实现对快速移动物体跟踪的技术。该算法在几个超声图像序列上进行了测试，每个序列涵盖一个完整的心动周期。轮廓成功跟踪了二尖瓣叶、主动脉根部和左心室内膜边界。将算法生成的轮廓与专家医生的手动描记进行了比较。自动方法生成的轮廓在观察者内变异性范围内。
