In this paper, we propose a level-set based segmentation method for medical images with
intensity inhomogeneity. Maximum a Posteriori estimation is adopted to combine image …

Image segmentation plays an important role in many medical imaging systems, yet in
complex circumstances it remains an open problem. One of the main difficulties is the …

This paper presents a novel correntropy-based level set method (CLSM) for medical image
segmentation and bias field correction. Firstly, we build a local bias-field-corrected fitting …

In this paper, integrating boundary and region information of medical images, we propose a
novel hybrid segmentation method based on level set. The main contributions of this paper …

The level set framework has proven well suited to medical image segmentation 1-6 thanks to
its ability of balancing the contribution of image data and prior knowledge in a principled …

This study proposes a local bias field and difference estimation (LBDE) model for medical
image segmentation and bias field correction. Firstly, the LBDE model uses a linear …

This paper presents a novel active contour model for simultaneous segmentation and bias
field estimation of medical images. Based on the additive model of images with intensity in …

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 …

This paper presents a new hybrid speed function needed to perform image segmentation
within the level-set framework. The proposed speed function uses both the boundary and …

Image segmentation has always been a considerable challenge in image analysis and
understanding due to the intensity inhomogeneity, which is also commonly known as bias …