MR image segmentation using phase information and a novel multiscale scheme

Authors:
Pierrick Bourgeat;Jurgen Fripp;Peter Stanwell;Saadallah Ramadan;Sébastien Ourselin
Affiliations:
BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Sydney, Australia;BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Sydney, Australia;Institute for Magnetic Resonance Research, Sydney, Australia;Institute for Magnetic Resonance Research, Sydney, Australia;BioMedIA Lab, Autonomous Systems Laboratory, CSIRO ICT Centre, Sydney, Australia
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2006

Citing 7
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Segmentation of Bone in Clinical Knee MRI Using Texture-Based Geodesic Active Contours

MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
An adaptive error penalization method for training an efficient and generalized SVM

Pattern Recognition
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
The use of unwrapped phase in MR image segmentation: a preliminary study

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Multiscale image segmentation using wavelet-domain hidden Markov models

IEEE Transactions on Image Processing
Adaptive scale fixing for multiscale texture segmentation

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.