A novel approach for global lung registration using 3d markov-gibbs appearance model

  • Authors:

  • Ayman El-Baz;Fahmi Khalifa;Ahmed Elnakib;Matthew Nitzken;Ahmed Soliman;Patrick McClure;Mohamed Abou El-Ghar;Georgy Gimel'farb

  • Affiliations:

  • BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;Radiology Department, Urology and Nephrology Center, University of Mansoura, Mansoura, Egypt;Department of Computer Science, University of Auckland, Auckland, New Zealand

  • Venue:
  • MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
  • Year:
  • 2012

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Abstract

A new approach to align 3D CT data of a segmented lung object with a given prototype (reference lung object) using an affine transformation is proposed. Visual appearance of the lung from CT images, after equalizing their signals, is modeled with a new 3D Markov-Gibbs random field (MGRF) with pairwise interaction model. Similarity to the prototype is measured by a Gibbs energy of signal co-occurrences in a characteristic subset of voxel pairs derived automatically from the prototype. An object is aligned by an affine transformation maximizing the similarity by using an automatic initialization followed by a gradient search. Experiments confirm that our approach aligns complex objects better than popular conventional algorithms.