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 brain magnetic resonance (MR) image has an embedded bias field. This field needs to
be corrected to obtain the actual MR image for classification. Bias field, being a slowly …

In this paper, we propose a novel Retinex induced piecewise constant variational model for
simultaneous segmentation of images with intensity inhomogeneity and bias correction …

The sonogram is currently an effective cancer screening and diagnosis way due to the
convenience and harmlessness in humans. Traditionally, lesion boundary segmentation is …

We present an accurate and fast approach for MR-image segmentation of brain tissues, that
is robust to anatomical variations and takes an average of less than 1min for completion on …

As a low-frequency and smooth signal, the bias field has a certain destructive effect on
magnetic resonance (MR) images and is the main obstacle for doctors' diagnosis and image …

Muscle volume measurements are essential for an array of diseases ranging from peripheral
arterial disease, muscular dystrophies, neurological conditions to sport injuries and aging. In …

This paper presents a variational model for simultaneous multiphase segmentation and
intensity bias estimation for images corrupted by strong noise and intensity inhomogeneity …

Picture fuzzy C-means clustering is a new computational intelligence method that has more
significant potential advantages than fuzzy clustering in medical image interpretation …

An effective retrospective correction method is introduced in this paper for intensity
inhomogeneity which is an inherent artifact in MR images. Intensity inhomogeneity problem …