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Pattern Analysis and Applications
Published in: Pattern Analysis and Applications 2/2009

01-06-2009 | Theoretical Advances

Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images

Authors: Yangqiu Song, Changshui Zhang, Jianguo Lee, Fei Wang, Shiming Xiang, Dan Zhang

Published in: Pattern Analysis and Applications | Issue 2/2009

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Abstract

Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, tumor segmentation work is difficult. This paper introduces a discriminative classification algorithm for semi-automated segmentation of brain tumorous tissues. The classifier uses interactive hints to obtain models to classify normal and tumor tissues. A non-parametric Bayesian Gaussian random field in the semi-supervised mode is implemented. Our approach uses both labeled data and a subset of unlabeled data sampling from 2D/3D images for training the model. Fast algorithm is also developed. Experiments show that our approach produces satisfactory segmentation results comparing to the manually labeled results by experts.
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Appendix
Available only for authorised users
Footnotes
1
Semi-supervised methods could be either transductive or inductive [32, 33]. While a transductive method only works on the observed labeled and unlabeled training data, the inductive methods can naturally handle the unseen data that are not in training set [33].
 
2
For the positive semi-definite case, we can add extra regularization as the jitter noise [59].
 
3
Namely, if we want to induce \({\varvec{\Updelta}}_{N+1}\) from \({\varvec{\Updelta}}_N\) directly, it need compute D ii of each new give point. This is very time consuming.
 
4
The weight matrix is near semi-positive definite, so we use the pseudo-inverse or add the extra regularization to find the square root of A in practice.
 
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Metadata
Title
Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images
Authors
Yangqiu Song
Changshui Zhang
Jianguo Lee
Fei Wang
Shiming Xiang
Dan Zhang
Publication date
01-06-2009
Publisher
Springer-Verlag
Published in
Pattern Analysis and Applications / Issue 2/2009
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-008-0104-3

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