Adaptive mesh generation for image registration and segmentation

Mads Fogtmann; Rasmus Larsen

doi:10.1109/ICIP.2013.6738156

Adaptive mesh generation for image registration and segmentation

Mads Fogtmann, Rasmus Larsen

University of Washington

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This paper deals with the problem of generating quality tetrahedral meshes for image registration. From an initial coarse mesh the approach matches the mesh to the image volume by combining red-green subdivision and mesh evolution through mesh-to-image matching regularized with a mesh quality measure. The method was tested on a T1 weighted MR volume of an adult brain and showed a 66% reduction in the number of mesh vertices compared to a red-subdivision strategy. The deformation capability of the mesh was tested by registration to five additional T1-weighted MR volumes.

Original language	English
Title of host publication	2013 20th IEEE International Conference on Image Processing (ICIP)
Publisher	IEEE
Publication date	2013
Pages	757-760
ISBN (Print)	978-1-4799-2341-0
DOIs	https://doi.org/10.1109/ICIP.2013.6738156
Publication status	Published - 2013
Event	2013 IEEE International Conference on Image Processing: IEEE ICIP 2013 - Melbourne, Australia Duration: 15 Sept 2013 → 18 Sept 2013 http://www.2013.ieeeicip.org/ICIP2013/

Conference

Conference	2013 IEEE International Conference on Image Processing
Country/Territory	Australia
City	Melbourne
Period	15/09/2013 → 18/09/2013
Internet address	http://www.2013.ieeeicip.org/ICIP2013/

Access to Document

10.1109/ICIP.2013.6738156

OpenUrl availability

Full text

Cite this

@inproceedings{eb46a1b5465a43959d48e47ae0791a37,

title = "Adaptive mesh generation for image registration and segmentation",

abstract = "This paper deals with the problem of generating quality tetrahedral meshes for image registration. From an initial coarse mesh the approach matches the mesh to the image volume by combining red-green subdivision and mesh evolution through mesh-to-image matching regularized with a mesh quality measure. The method was tested on a T1 weighted MR volume of an adult brain and showed a 66% reduction in the number of mesh vertices compared to a red-subdivision strategy. The deformation capability of the mesh was tested by registration to five additional T1-weighted MR volumes.",

author = "Mads Fogtmann and Rasmus Larsen",

year = "2013",

doi = "10.1109/ICIP.2013.6738156",

language = "English",

isbn = "978-1-4799-2341-0",

pages = "757--760",

booktitle = "2013 20th IEEE International Conference on Image Processing (ICIP)",

publisher = "IEEE",

address = "United States",

note = "2013 IEEE International Conference on Image Processing : IEEE ICIP 2013 ; Conference date: 15-09-2013 Through 18-09-2013",

url = "http://www.2013.ieeeicip.org/ICIP2013/",

}

TY - GEN

T1 - Adaptive mesh generation for image registration and segmentation

AU - Fogtmann, Mads

AU - Larsen, Rasmus

PY - 2013

Y1 - 2013

N2 - This paper deals with the problem of generating quality tetrahedral meshes for image registration. From an initial coarse mesh the approach matches the mesh to the image volume by combining red-green subdivision and mesh evolution through mesh-to-image matching regularized with a mesh quality measure. The method was tested on a T1 weighted MR volume of an adult brain and showed a 66% reduction in the number of mesh vertices compared to a red-subdivision strategy. The deformation capability of the mesh was tested by registration to five additional T1-weighted MR volumes.

AB - This paper deals with the problem of generating quality tetrahedral meshes for image registration. From an initial coarse mesh the approach matches the mesh to the image volume by combining red-green subdivision and mesh evolution through mesh-to-image matching regularized with a mesh quality measure. The method was tested on a T1 weighted MR volume of an adult brain and showed a 66% reduction in the number of mesh vertices compared to a red-subdivision strategy. The deformation capability of the mesh was tested by registration to five additional T1-weighted MR volumes.

U2 - 10.1109/ICIP.2013.6738156

DO - 10.1109/ICIP.2013.6738156

M3 - Article in proceedings

SN - 978-1-4799-2341-0

SP - 757

EP - 760

BT - 2013 20th IEEE International Conference on Image Processing (ICIP)

PB - IEEE

T2 - 2013 IEEE International Conference on Image Processing

Y2 - 15 September 2013 through 18 September 2013

ER -

Adaptive mesh generation for image registration and segmentation

Abstract

Conference

Access to Document

OpenUrl availability

Fingerprint

Cite this