title: Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.
abstract: We propose to use the sparseness property of the gradient probability distribution to estimate the intensity nonuniformity in medical images, resulting in two novel automatic methods: a non-parametric method and a parametric method. Our methods are easy to implement because they both solve an iteratively re-weighted least squares problem. They are remarkably accurate as shown by our experiments on images of different imaged objects and from different imaging modalities.
authors.affiliation: Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: Yuanjie
authors.initials: Y
authors.lastname: Zheng
authors.forename: Murray
authors.initials: M
authors.lastname: Grossman
authors.forename: Suyash P
authors.initials: SP
authors.lastname: Awate
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity