In this research three innovative registration systems were designed with the configurations of the mutual information and optimization technique: (1) mutual information combined with the downhill simplex method of optimization. (2) the derivative of mutual information combined with Quasi-Newton method. (3) mutual information combined with hybrid genetic algorithm (large-space random search) to avoid local maximum during the optimization. These automatic registration systems were evaluated with a variety of images, dimensions and voxel resolutions. Experiments demonstrate that registration system combined with mutual information and hybrid genetic algorithm can provide robust and accurate alignments to obtain a composite activation map for functional MRI analysis.

Creator

• Yu, Hongliang

Contributori

• Sullivan, John M.
• Ludwig, Reinhold
• Tryggvason, Grétar
• Hoffman, Allen H.

Degree

• PhD

Unit

• Mechanical Engineering

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-050905-100341

Parola chiave

• Image Registration

Advisor

• Sullivan, John M.

Committee

• Hoffman, Allen H.
• Ludwig, Reinhold
• Tryggvason, Gretar

Defense date

• 2005-05-04

Year

• 2005

Date created

• 2005-05-09

Resource type

• Dissertation

Rights statement

• In Copyright

Ultima modifica

• 2020-12-31