Segmentationof pathological lungs from CT chest images

Ahmed Soliman, Ahmed Elnakib, Fahmi Khalifa, Mohamed Abou El-Ghar, Ayman El-Baz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


A novel framework for precise segmentation of pathological lung tissues from computed tomography (CT) is presented. The proposed segmentation method is based on a novel 3D joint Markov-Gibbs random field (MGRF) model that integrates three features: (i) the first-order visual appearance model of the CT image, (ii) the second-order spatial interaction model of the CT image, and (iii) a shape prior model of the lung. The first-order appearance model describes the empirical distribution of image signals using a linear combination of Discrete Gaussians (LCDG) with positive and negative components. The second order spatial interaction model describes the relation between the CT image signals using a pairwise MGRF spatial model of independent image signals and interdependent region labels. The shape prior is constructed from a set of training CT data, collected from different subjects. Experiments on 20 datasets with different types of pathologies confirm high accuracy of the proposed approach compared with other lung segmentation methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


OtherIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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