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Entropic graphs for image registration.

Neemuchwala, Huzefa Firoz.

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Entropic graphs for image registration.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Entropic graphs for image registration./
作者:	Neemuchwala, Huzefa Firoz.
面頁冊數:	194 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1019.
Contained By:	Dissertation Abstracts International66-02B.
標題:	Engineering, Biomedical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3163898
ISBN:	0496984950

Entropic graphs for image registration.
Neemuchwala, Huzefa Firoz.

Entropic graphs for image registration. - 194 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1019.

Thesis (Ph.D.)--University of Michigan, 2005.

Given 2D or 3D images gathered via multiple sensors located at different positions, the multi-sensor image registration problem is to align the images so that they have an identical pose in a common coordinate system. Image registration methods depend crucially upon a robust image similarity measure to guide the image alignment. This thesis concerns itself with a new class of such similarity measures. The launching point of this thesis is the entropic graph based estimate of Renyi's alpha-entropy developed by Ma for image registration. This thesis extends this initial work to develop other entropic graph-based divergence measures to be used with advanced higher dimensional features. A detailed analysis of entropic graphs is followed by a demonstration of their performance advantages relative to conventional similarity measures. This thesis introduces techniques to extend image registration to higher dimension feature spaces using Renyi's generalized alpha-entropy. The alpha-entropy is estimated directly through continuous quasi-additive power-weighted graphs such as the minimal spanning tree (MST) and k-Nearest Neighbor graph (kNN). Entropic graph methods are further used to approximate similarity measures like the alpha-mutual information, non-linear correlation coefficient, alpha-Jensen divergence, Henze-Penrose affinity and Geometric-Arithmetic mean affinity. Entropic-graph similarity measures are applied to problems in breast Ultrasound image registration for cancer management, geo-stationary satellite registration, feature clustering and classification and for atlas based multi-image registration. This last work is a novel and significant application of divergence estimation for registering several images simultaneously. These similarity measures offer robust registration benefits in a multisensor environment. Higher dimensional features used for this work include basis functions like multidimensional wavelets, independent component analysis (ICA) and discrete cosine transforms.

ISBN: 0496984950Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Entropic graphs for image registration.
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502 $a Thesis (Ph.D.)--University of Michigan, 2005.
520 $a Given 2D or 3D images gathered via multiple sensors located at different positions, the multi-sensor image registration problem is to align the images so that they have an identical pose in a common coordinate system. Image registration methods depend crucially upon a robust image similarity measure to guide the image alignment. This thesis concerns itself with a new class of such similarity measures. The launching point of this thesis is the entropic graph based estimate of Renyi's alpha-entropy developed by Ma for image registration. This thesis extends this initial work to develop other entropic graph-based divergence measures to be used with advanced higher dimensional features. A detailed analysis of entropic graphs is followed by a demonstration of their performance advantages relative to conventional similarity measures. This thesis introduces techniques to extend image registration to higher dimension feature spaces using Renyi's generalized alpha-entropy. The alpha-entropy is estimated directly through continuous quasi-additive power-weighted graphs such as the minimal spanning tree (MST) and k-Nearest Neighbor graph (kNN). Entropic graph methods are further used to approximate similarity measures like the alpha-mutual information, non-linear correlation coefficient, alpha-Jensen divergence, Henze-Penrose affinity and Geometric-Arithmetic mean affinity. Entropic-graph similarity measures are applied to problems in breast Ultrasound image registration for cancer management, geo-stationary satellite registration, feature clustering and classification and for atlas based multi-image registration. This last work is a novel and significant application of divergence estimation for registering several images simultaneously. These similarity measures offer robust registration benefits in a multisensor environment. Higher dimensional features used for this work include basis functions like multidimensional wavelets, independent component analysis (ICA) and discrete cosine transforms.
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