Image segmentation is a wide research topic; a huge amount of research has been
performed in this context. Image segmentation is a crucial procedure for most object …

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 …

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 …

This paper raises a region-edge-based active contour driven by the hybrid and local fuzzy
region-based energy to segment images with high noise and intensity inhomogeneity. The …

This paper presents a novel global and local fuzzy image fitting (GLFIF) based active
contour model for image segmentation. First, we design two fitted images: global fuzzy fitted …

Medical image segmentation has a huge challenge due to intensity inhomogeneity and the
similarity of the background and the object. To meet this challenge, we propose an improved …