Registration And Fusion Techniques For Multimodal Brain Images

dc.contributor.author	Al-Azzawi, Nemir Ahmed Fuaad
dc.date.accessioned	2018-07-09T07:19:36Z
dc.date.available	2018-07-09T07:19:36Z
dc.date.issued	2010-08
dc.description.abstract	The research presented in this thesis is concerned with the problem of multimodal medical image registration and fusion for brain images. The work on image registration conducted in this thesis was based on feature and intensity. The feature-based approach used nonsubsampled contourlet transfrm (NSCT) to extract salient edges and control points. Then, mutual information was adopted to the register feature points where translation parameters were calculated by using particle swarm optimization. As result, the registration performance was perfect. The intensity-based nonrigid registration was developed and evaluated based on free-form deformation (FFD). Kullback-Leibler distance (KLD) and particle swarm optimization (PSO). The procedure yielded quality images with average target registration error (TRE) below 4 mm. Furthermore, the proposed method could run 2-3 times faster than the traditional nonrigid registration mutual imformation method.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/5891
dc.language.iso	en	en_US
dc.publisher	Universiti Sains Malaysia	en_US
dc.subject	Registration and fusion techniques	en_US
dc.subject	for multimodal brain images	en_US
dc.title	Registration And Fusion Techniques For Multimodal Brain Images	en_US
dc.type	Thesis	en_US

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