Bias field in medical images is an undesirable artifact primarily arises from the improper
image acquisition process or the specific properties of the imaged object. This artifact can be …

The fuzzy C-means (FCM) clustering procedure is an unsupervised form of grou** the
homogenous pixels of an image in the feature space into clusters. A brain magnetic …

Computational imaging plays an important role in medical treatment for providing more
comprehensive medical images. This work proposes a new scheme to fuse computed …

Image segmentation is fundamental and particularly important for computer vision and
pattern recognition. However, it is still not a completely resolved problem due to the …

The Mumford-Shah model is an important tool for image labeling and segmentation, which
pursues a piecewise smooth approximation of the original image and the boundaries with …

Brain extraction is an essential pre-processing step for neuroimaging analysis. It is difficult to
achieve high-precision extraction from low-quality brain MRI images with artifacts and gray …

Natural images are usually composed of multiple objects at different scales in flat and
slanted regions. Traditional labeling/segmentation approaches based on total variation …

In this paper, we present a new variant of level set methods and then propose a ternary
variational level set model involving L 0 gradient regularizer and L 0 function regularizer in …

Rician noise and intensity nonuniformity are two common artifacts and usually coexist in
magnetic resonance imaging (MRI) data. Many methods have been proposed in the …

In this article, we propose a new variational model for segmenting images with intensity
inhomogeneity. The proposed model applies simultaneously the local constant and global …