Chapter title |
A Novel Approach for Global Lung Registration Using 3D Markov-Gibbs Appearance Model
|
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Chapter number | 15 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
|
Published in |
Lecture notes in computer science, January 2012
|
DOI | 10.1007/978-3-642-33418-4_15 |
Pubmed ID | |
Book ISBNs |
978-3-64-233417-7, 978-3-64-233418-4
|
Authors |
El-Baz, Ayman, Khalifa, Fahmi, Elnakib, Ahmed, Nitzken, Matthew, Soliman, Ahmed, McClure, Patrick, El-Ghar, Mohamed Abou, Gimel’farb, Georgy, Ayman El-Baz, Fahmi Khalifa, Ahmed Elnakib, Matthew Nitzken, Ahmed Soliman, Patrick McClure, Mohamed Abou El-Ghar, Georgy Gimel’farb |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 4% |
Unknown | 22 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 7 | 30% |
Student > Ph. D. Student | 5 | 22% |
Student > Master | 3 | 13% |
Other | 2 | 9% |
Lecturer | 1 | 4% |
Other | 2 | 9% |
Unknown | 3 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 7 | 30% |
Engineering | 6 | 26% |
Medicine and Dentistry | 4 | 17% |
Agricultural and Biological Sciences | 1 | 4% |
Unknown | 5 | 22% |