Paper Info
Title
Semi-supervised discriminative classification with application to tumorous tissues segmentation of MR brain images
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.
Year
DOI
Venue
2009
10.1007/s10044-008-0104-3
Pattern Anal. Appl.
Keywords
Field
DocType
large data size,magnetic resonance imaging mri� brain tumor segmentationsemi-automated segmentationgaussian random field grf� gaussian process gp,semi-supervised discriminative classification,tumor segmentation work,satisfactory segmentation result,discriminative classification algorithm,tumor tissue,mr brain image,semi-automated segmentation,fast algorithm,brain tumorous tissue,unlabeled data,tumorous tissues segmentation,random field,3d imaging,gaussian process,gaussian random field,brain imaging,magnetic resonance image
Computer vision,Scale-space segmentation,Gaussian random field,Pattern recognition,Segmentation,Artificial intelligence,Gaussian process,Labeled data,Classifier (linguistics),Discriminative model,Mathematics,Bayesian probability
Journal
Volume
Issue
ISSN
12
2
1433-755X
Citations 
PageRank 
References 
6
0.58
50
Authors
6
Name
Order
Citations
PageRank
Yangqiu Song12045103.29
Changshui Zhang25506323.40
Jianguo Lee3222.48
Fei Wang42139135.03
Shiming Xiang52136110.53
Dan Zhang646122.17