title: A novel approach for global lung registration using 3D Markov-Gibbs appearance model.
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.
authors.affiliation: BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA.
authors.forename: Ayman
authors.initials: A
authors.lastname: El-Baz
authors.forename: Fahmi
authors.initials: F
authors.lastname: Khalifa
authors.forename: Ahmed
authors.initials: A
authors.lastname: Elnakib
authors.forename: Matthew
authors.initials: M
authors.lastname: Nitzken
authors.forename: Ahmed
authors.initials: A
authors.lastname: Soliman
authors.forename: Patrick
authors.initials: P
authors.lastname: McClure
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abou El-Ghar
authors.forename: Georgy
authors.initials: G
authors.lastname: Gimel'farb
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique