Yezzi A, Kichenassamy S, Kumar A, Olver P, Tannenbaum A
Department of Electrical Engineering, University of Minnesota, Minneapolis 55455, USA.
IEEE Trans Med Imaging. 1997 Apr;16(2):199-209. doi: 10.1109/42.563665.
In this note, we employ the new geometric active contour models formulated in [25] and [26] for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature.
在本笔记中，我们采用了在[25]和[26]中提出的新几何主动轮廓模型，用于磁共振成像（MRI）、计算机断层扫描（CT）和超声医学图像的边缘检测与分割。我们的方法基于在给定图像上定义基于特征的度量，这反过来又导致了一种新颖的蛇形范式，其中感兴趣的特征可以被认为位于势阱底部。因此，蛇形模型能够非常快速且高效地被吸引到所需特征处。
