Deformable registration of cortical structures via hybrid volumetric and surface warping

Tianming Liu; Dinggang Shen; Christos Davatzikos

doi:10.1007/978-3-540-39903-2_95

Deformable registration of cortical structures via hybrid volumetric and surface warping

Tianming Liu^*
, Dinggang Shen
, Christos Davatzikos

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

This paper presents a method of deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. The proposed method uses two steps. In the first step, a HAMMER-based volumetric registration algorithm warps the model surface to the individual's space. In the second step, an attribute-based surface registration method further refines the results of the volumetric warping. An attribute vector is defined for each vertex on the cortical surface, and used to capture the local and global geometric features of the surface patch. The attribute vector is designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on synthesized and real brain data are provided to demonstrate the performance of the proposed method in registering cortical structures across individuals.

Original language	English
Pages (from-to)	780-787
Number of pages	8
Journal	Lecture Notes in Computer Science
Volume	2879
Issue number	PART 2
DOIs	https://doi.org/10.1007/978-3-540-39903-2_95
Publication status	Published - 2003
Event	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings - Montreal, Que., Canada Duration: 2003 Nov 15 → 2003 Nov 18

Bibliographical note

Funding Information:
This work was supported by NIH grant R01 AG14971 and by NIH grant R01 NS42645. Images were acquired as part of the neuroimaging study of the Baltimore Longitudinal Study of Aging (BLSA).

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-540-39903-2_95

Cite this

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title = "Deformable registration of cortical structures via hybrid volumetric and surface warping",

abstract = "This paper presents a method of deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. The proposed method uses two steps. In the first step, a HAMMER-based volumetric registration algorithm warps the model surface to the individual's space. In the second step, an attribute-based surface registration method further refines the results of the volumetric warping. An attribute vector is defined for each vertex on the cortical surface, and used to capture the local and global geometric features of the surface patch. The attribute vector is designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on synthesized and real brain data are provided to demonstrate the performance of the proposed method in registering cortical structures across individuals.",

author = "Tianming Liu and Dinggang Shen and Christos Davatzikos",

note = "Funding Information: This work was supported by NIH grant R01 AG14971 and by NIH grant R01 NS42645. Images were acquired as part of the neuroimaging study of the Baltimore Longitudinal Study of Aging (BLSA).; Medical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings ; Conference date: 15-11-2003 Through 18-11-2003",

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AU - Shen, Dinggang

AU - Davatzikos, Christos

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N2 - This paper presents a method of deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. The proposed method uses two steps. In the first step, a HAMMER-based volumetric registration algorithm warps the model surface to the individual's space. In the second step, an attribute-based surface registration method further refines the results of the volumetric warping. An attribute vector is defined for each vertex on the cortical surface, and used to capture the local and global geometric features of the surface patch. The attribute vector is designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on synthesized and real brain data are provided to demonstrate the performance of the proposed method in registering cortical structures across individuals.

AB - This paper presents a method of deformable registration of cortical structures across individuals, using hybrid volumetric and surface warping. The proposed method uses two steps. In the first step, a HAMMER-based volumetric registration algorithm warps the model surface to the individual's space. In the second step, an attribute-based surface registration method further refines the results of the volumetric warping. An attribute vector is defined for each vertex on the cortical surface, and used to capture the local and global geometric features of the surface patch. The attribute vector is designed to be as distinctive as possible, so that each vertex on the model surface can find its correspondence on the individual surface. Experimental results on synthesized and real brain data are provided to demonstrate the performance of the proposed method in registering cortical structures across individuals.

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Deformable registration of cortical structures via hybrid volumetric and surface warping

Abstract

Bibliographical note

ASJC Scopus subject areas

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