论文标题
一种基于特征函数的算法，用于测量活性轮廓
A Characteristic Function-based Algorithm for Geodesic Active Contours
论文作者
论文摘要
主动轮廓模型已在图像分割中广泛使用，级别集方法（LSM）是解决模型的最流行方法，通过隐式表示通过级别集合函数表示轮廓。但是，LSM遭受了高计算负担和数值不稳定性，需要其他正则化术语或重新定位技术。在本文中，我们使用特征函数隐式表示轮廓，向地球活动轮廓提出了新的表示，并得出了一种有效的算法，称为迭代卷积阈值阈值方法（ICTM）。与LSM相比，ICTM更简单，更有效。此外，ICTM享有基于级别设置的方法的最期望的功能。在2D合成，2D超声，3D CT和3D MR图像上进行的广泛实验表明，所提出的方法不仅获得了可比甚至更好的分割结果（与LSM相比），而且可以达到显着加速。
Active contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function. However, the LSM suffers from high computational burden and numerical instability, requiring additional regularization terms or re-initialization techniques. In this paper, we use characteristic functions to implicitly represent the contours, propose a new representation to the geodesic active contours and derive an efficient algorithm termed as the iterative convolution-thresholding method (ICTM). Compared to the LSM, the ICTM is simpler and much more efficient. In addition, the ICTM enjoys most desired features of the level set-based methods. Extensive experiments, on 2D synthetic, 2D ultrasound, 3D CT, and 3D MR images for nodule, organ and lesion segmentation, demonstrate that the proposed method not only obtains comparable or even better segmentation results (compared to the LSM) but also achieves significant acceleration.
