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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.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC3673007 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images.

Zheng Yuanjie Y Grossman Murray M Awate Suyash P SP Gee James C JC

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20090101 Pt 2

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. ...[more]

PMID: 20426191

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