Department of Computer Engineering, Kyung Hee University, Repuplic of Korea.
Comput Biol Med. 2011 May;41(5):292-301. doi: 10.1016/j.compbiomed.2011.03.006. Epub 2011 Apr 9.
In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan-Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast.
本文提出了一种新的基于凸组合的主动轮廓模型用于医学图像分割，该模型同时最小化目标内的不均匀性和最大化目标与背景之间的距离。这种组合是必要的，因为医学图像中的目标，例如骨骼，通常是高度不均匀的，而不同的器官应该产生不同的图像配置。与传统的 Chan-Vese 主动轮廓模型相比，所提出的模型在一组 CT 图像上具有相似的性能，但在对比度通常较低的 MRI 数据集上表现更好。
