A Novel Approach for Global Lung Registration Using 3D Markov-Gibbs Appearance Model

被引：0

作者：

El-Baz, Ayman ^{[1
]}

Khalifa, Fahmi ^{[1
]}

Elnakib, Ahmed ^{[1
]}

Nitzken, Matthew ^{[1
]}

Soliman, Ahmed ^{[1
]}

McClure, Patrick ^{[1
]}

Abou El-Ghar, Mohamed ^{[2
]}

Gimel'farb, Georgy ^{[3
]}

机构：

[1] Univ Louisville, Dept Bioengn, Biolmaging Lab, Louisville, KY 40292 USA

[2] Univ Mansoura, Dept Radiol, Urol & Nephrol Ctr, Mansoura 35516, Egypt

[3] Univ Auckland, Dept Comp Sci, Auckland 1, New Zealand

来源：

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2012, PT II | 2012年 / 7511卷

关键词：

IMAGES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：114 / 121

页数：8

共 12 条

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