The active contour model (ACM) approach in image segmentation is regarded as a research
hotspot in the area of computer vision, which is widely applied in different kinds of …

Intensity inhomogeneity brings great difficulties to image segmentation. This problem is
partly solved by the multiplicative bias field correction model. However, some other …

Currently, there is an urgent need to utilize automatic approaches to detecting pavement
cracks for roadway maintenance. Taking advantage of the development of convolutional …

Active contour model (ACM) has been a competitive tool in image segmentation because of
its desired segmentation result and accuracy. Nevertheless, it may become unstable while …

Medical image segmentation is a very challenging task, not only because the intensity of the
medical image itself is not uniform, but also it may be accompanied by the impact of noise …

The lack of grasp of the image information and the unstable fluctuation of the model energy
may cause segmentation failure of the active contour model (ACM). Minimizing the impact of …

In this study, a hybrid active contour model driven by pre-fitting energy with an adaptive
edge indicator function and an adaptive sign function is proposed. The key idea of …

Image segmentation is a fundamental and tedious task of computer vision. Because of
inherent noise and intensity inhomogeneity in real-world images, it remains a difficult …

Local fitting-based active contour models can segment images with intensity inhomogeneity
effectively, but they are time-consuming and often fall into local minima. In this paper, we …

Accurate segmentation is difficult for liver computed tomography (CT) images, since the liver
CT images do not always have obvious and smooth boundaries. The location of the tumor is …