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Appearance Models for Robust Segmentation of Pulmonary Nodules in 3D LDCT Chest Images

  • Aly A. Farag
  • Ayman El-Baz
  • Georgy Gimel’farb
  • Robert Falk
  • Mohamed A. El-Ghar
  • Tarek Eldiasty
  • Salwa Elshazly
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

To more accurately separate each pulmonary nodule from its background in a low dose computer tomography (LDCT) chest image, two new adaptive probability models of visual appearance of small 2D and large 3D pulmonary nodules are used to control evolution of deformable boundaries. The appearance prior is modeled with a translation and rotation invariant Markov-Gibbs random field of voxel intensities with pairwise interaction analytically identified from a set of training nodules. Appearance of the nodules and their background in a current multi-modal chest image is also represented with a marginal probability distribution of voxel intensities. The nodule appearance model is isolated from the mixed distribution using its close approximation with a linear combination of discrete Gaussians. Experiments with real LDCT chest images confirm high accuracy of the proposed approach.

Keywords

Pulmonary Nodule Appearance Model Deformable Model Voxel Intensity Marginal Probability Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aly A. Farag
    • 1
  • Ayman El-Baz
    • 1
  • Georgy Gimel’farb
    • 2
  • Robert Falk
    • 3
  • Mohamed A. El-Ghar
    • 4
  • Tarek Eldiasty
    • 4
  • Salwa Elshazly
    • 1
  1. 1.Computer Vision and Image Processing LaboratoryUniversity of LouisvilleLouisville
  2. 2.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  3. 3.Director, Medical Imaging DivisionJewish Hospital 
  4. 4.Urology and Nephrology DepartmentUniversity of MansouraMansouraEgypt

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