Shang Yanfeng, Yang Xin, Zhu Lei, Deklerck Rudi, Nyssen Edgard
Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, PR China.
Comput Med Imaging Graph. 2008 Mar;32(2):109-17. doi: 10.1016/j.compmedimag.2007.10.004.
In this paper, a probabilistic and level set model for three-dimensional medical object extraction is proposed, which is called region competition based active contour. The algorithms are derived by minimizing a region based probabilistic energy function and implemented in a level set framework. An additional speed-controlling term makes the active contour quickly convergent to the actual contour on strong edges, whereas a probabilistic model makes the active contour performing well for weak edges. Prior knowledge about the initial contour and the probabilistic distribution contributes to more efficient extraction. The developed model has been applied to a variety of medical images, from CTA and MRA of the coronary to rotationally scanned and real-time three-dimensional echocardiography images of the mitral valve. As the results show, the algorithm is fast, convergent, adapted to a broad range of medical objects and produces satisfactory results.
本文提出了一种用于三维医学对象提取的概率和水平集模型，称为基于区域竞争的活动轮廓模型。该算法通过最小化基于区域的概率能量函数推导得出，并在水平集框架中实现。一个额外的速度控制项使活动轮廓在强边缘上快速收敛到实际轮廓，而概率模型使活动轮廓在弱边缘上表现良好。关于初始轮廓和概率分布的先验知识有助于更有效地进行提取。所开发的模型已应用于各种医学图像，从冠状动脉的CTA和MRA到二尖瓣的旋转扫描和实时三维超声心动图图像。结果表明，该算法速度快、收敛性好，适用于广泛的医学对象，并产生了令人满意的结果。
