Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.
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.
Zheng
Yuanjie
Y
Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
Grossman
Murray
M
Awate
Suyash P
SP
Gee
James C
JC
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