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 …

In this paper, we propose an improved Chan–Vese (CV) model that uses Kullback–Leibler
(KL) distances and local neighborhood information (LNI). Due to the effects of heterogeneity …

Automated and general medical image segmentation can be challenging because the
foreground and the background may have complicated and overlapping density distributions …

Intensity inhomogeneity and noise are two common issues in images but inevitably lead to
significant challenges for image segmentation and is particularly pronounced when the two …

Appropriate initialization and stable evolution are desirable criteria to satisfy in level set
methods. In this study, a novel region-based level set method utilizing both global and local …

The level set method is widely used in medical image segmentation, in which the
performance is seriously subject to the initialization and parameters configuration. An …

Abstract medical images are more affected by intensity inhomogeneity rather than noise and
outliers. This has a great impact on the efficiency of region-based image segmentation …

Level set methods have been widely used to implement active contours for image
segmentation applications due to their good boundary detection accuracy. In the context of …

Intensity inhomogeneity often appears in medical images, such as X-ray tomography and
magnetic resonance (MR) images, due to technical limitations or artifacts introduced by the …

Intensity inhomogeneity and high noise are two common but challenging issues in image
segmentation and is particularly pronounced when the two issues simultaneously appear in …