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Pattern Analysis and Applications
Erschienen 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

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

Erschienen in: Pattern Analysis and Applications | Ausgabe 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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Fußnoten
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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Metadaten
Titel
Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images
verfasst von
Yangqiu Song
Changshui Zhang
Jianguo Lee
Fei Wang
Shiming Xiang
Dan Zhang
Publikationsdatum
01.06.2009
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2009
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-008-0104-3

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