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 …

Image segmentation is a fundamental task in computer vision and image processing. Due to
the presence of high noise, low resolution and intensity inhomogeneity, it is still a difficult …

Intensity inhomogeneities and different types/levels of image noise are the two major
obstacles to accurate image segmentation by region-based level set models. To provide a …

It is often a difficult task to accurately segment images with intensity inhomogeneity, because
most of representative algorithms are region-based that depend on intensity homogeneity of …

Intensity inhomogeneity often occurs in real-world images, which presents a considerable
challenge in image segmentation. The most widely used image segmentation algorithms are …

Intensity inhomogeneity arising from the imperfect image acquisition process is a major
challenge for image segmentation. Most of widely used image segmentation methods …

In this paper, a level set model without the need of generating initial contour and setting
controlling parameters manually is proposed for medical image segmentation. The …

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 …

This paper presents a novel level set method for complex image segmentation, where the
local statistical analysis and global similarity measurement are both incorporated into the …

Images with intensity inhomogeneity pose significant challenges in image segmentation.
Local region-based level set models have recently been recognized as promising methods …