Multispectral MR Images Segmentation Using SOM Network
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Handbook of Biomedical Image Analysis: Volume 2: Segmentation Models Part B (Topics in Biomedical EngineeringInternational Book Series)
An EM based multiple instance learning method for image classification
Expert Systems with Applications: An International Journal
Pattern Analysis & Applications
Semi-supervised Tissue Segmentation of 3D Brain MR Images
IV '10 Proceedings of the 2010 14th International Conference Information Visualisation
Semi-supervised multi-class Adaboost by exploiting unlabeled data
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Magnetic resonance (MR) brain image segmentation of different anatomical structures or tissue types has become a critical requirement in the diagnosis of neurological diseases. Depending on the availability of the training samples, image segmentation can be either supervised or unsupervised. While supervised learning requires a sufficient amount of labelled training data, which is expensive and time-consuming, unsupervised learning techniques suffer from the problem of local traps. Semi-supervised algorithms that includes prior knowledge into the unsupervised learning can enhance the segmentation process without the need of labelled training data. This paper proposes a method to improve the quality of MR brain tissue segmentation and to accelerate the convergence process. The proposed method is a clustering based semi-supervised classifier that does not need a set of labelled training data and uses less human expert analysis than a supervised approach. The proposed classifier labels the voxels clusters of an image slice and then uses statistics and class labels information of the resultant clusters to classify the remaining image slices by applying Gaussian Mixture Model (GMM). The experimental results show that the proposed semi-supervised approach accelerates the convergence and improves the results accuracy when comparing with the classical GMM approach.