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

• Published in: Pattern analysis and applications : PAA Vol. 12; no. 2; pp. 99 - 115
• Main Authors: Song, Yangqiu, Zhang, Changshui, Lee, Jianguo, Wang, Fei, Xiang, Shiming, Zhang, Dan
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01.06.2009; Springer
• Subjects: Applied sciences; Artificial intelligence; Computer Science; Computer science; control theory; systems; Exact sciences and technology; Learning and adaptive systems; Pattern Recognition; Pattern recognition. Digital image processing. Computational geometry; Theoretical Advances; Gaussian process (GP); Magnetic resonance imaging (MRI); Brain tumor segmentation; Gaussian random field (GRF); Semi-automated segmentation; Bayes estimation; Human; Brain; Computer vision; Probabilistic approach; Segmentation; Non gaussian noise; Central nervous system; Nuclear magnetic resonance imaging; Modeling; Supervised classification; Encephalon; Tissue; Gaussian process; Gaussian field; Tridimensional image; Fast algorithm; Sampling
• ISSN: 1433-7541; 1433-755X
• DOI: 10.1007/s10044-008-0104-3

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.