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. © 2009 Springer-Verlag.
Cite
CITATION STYLE
Zheng, Y., Grossman, M., Awate, S. P., & Gee, J. C. (2009). Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5762 LNCS, pp. 852–859). https://doi.org/10.1007/978-3-642-04271-3_103
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