Intrinsic Random Fields and Image Deformations

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• 作者：Kanti V. Mardia ; Fred L. Bookstein ; John T. Kent and Charles R. Meyer
• 关键词：image deformations ; thin ; plate splines ; stochastic deformations ; stochastic increment processes ; registration of deformations ; intrinsic random fields ; maximum ; likelihood estimation of deformations ; estimation error for deformations ; self ; similarity ; fractal dimension of deformation noise
• 刊名：Journal of Mathematical Imaging and Vision
• 出版年：2006
• 出版时间：November, 2006
• 年：2006
• 卷：26
• 期：1-2
• 页码：59-71
• 全文大小：272 KB
• 刊物类别：Computer Science
• 刊物主题：Computer Imaging, Vision, Pattern Recognition and Graphics
  Image Processing and Computer Vision
  Artificial Intelligence and Robotics
  Automation and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1573-7683

文摘

The stochastic structure of images, especially individual medical images as they are reconstructed nowadays from arrays of medical imaging sensors, is becoming steadily better understood. Less attention has been paid to the parallel notion of estimation error for the deformations that convey relations among these images, such as localized abnormality or growth prediction. The dominant current formalism for the biostatistics of deformations deals solely with the shape of a set of landmarks parameterizing the deformation, not otherwise with its behaviour inbetween the landmarks. This paper attempts to fit a rigorous stochastic model for a deformation between landmarks and to assess the error of the fitted deformation. The relation between two images is modelled as a stochastic deformation, i.e. as an identity map plus a stochastic process whose value at every point is a vector-valued displacement.