Automatic Correction of Intensity Nonuniformity from Sparseness of Gradient Distribution in Medical Images

  • Authors:

  • Yuanjie Zheng;Murray Grossman;Suyash P. Awate;James C. Gee

  • Affiliations:

  • Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, USA;Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, USA;Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, USA;Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, USA

  • Venue:
  • MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
  • Year:
  • 2009

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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.