MR image segmentation using phase information and a novel multiscale scheme

Bourgeat, Pierrick, Fripp, Jurgen, Stanwell, Peter, Ramadan, Saadallah and Ourselin, Sebastien (2006). MR image segmentation using phase information and a novel multiscale scheme. In: R. Larsen, M. Nielson and J. Sporring, Lecture Notes in Computer Science: 9th International Conference Medical Image Computing and Computer-Assisted Intervention (MICCAI'06), Proceedings Part II. Medical Image Computing and Computer-Assisted Intervention: MICCAI 2006, Copenhagen, Denmark, (920-927). 1-6 October, 2006. doi:10.1007/11866763_113


Author Bourgeat, Pierrick
Fripp, Jurgen
Stanwell, Peter
Ramadan, Saadallah
Ourselin, Sebastien
Title of paper MR image segmentation using phase information and a novel multiscale scheme
Conference name Medical Image Computing and Computer-Assisted Intervention: MICCAI 2006
Conference location Copenhagen, Denmark
Conference dates 1-6 October, 2006
Proceedings title Lecture Notes in Computer Science: 9th International Conference Medical Image Computing and Computer-Assisted Intervention (MICCAI'06), Proceedings Part II   Check publisher's open access policy
Journal name Medical Image Computing and Computer-Assisted Intervention - Miccai 2006, Pt 2   Check publisher's open access policy
Place of Publication Berlin, Germany
Publisher Springer Verlag
Publication Year 2006
Sub-type Fully published paper
DOI 10.1007/11866763_113
ISBN 3-540-44727-X
ISSN 0302-9743
Editor R. Larsen
M. Nielson
J. Sporring
Volume 4191
Start page 920
End page 927
Total pages 8
Collection year 2006
Language eng
Formatted Abstract/Summary
This paper considers the problem of automatic classification of textured tissues in 3D MRI.

More specifically, it aims at validating the use of features extracted from the phase of the MR signal to improve texture discrimination in bone segmentation.

This extra information provides better segmentation, compared to using magnitude only features.

We also present a novel multiscale scheme to improve the speed of pixel based classification algorithm, such as support vector machines.

This algorithm dramatically increases the speed of the segmentation process by an order of magnitude through a reduction of the number of pixels that needs to be classified in the image.
Subjects E1
730114 Skeletal system and disorders (incl. arthritis)
321022 Radiology and Organ Imaging
321017 Orthopaedics
Q-Index Code E1
Institutional Status UQ

 
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Created: Thu, 23 Aug 2007, 22:11:40 EST