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. 2013 Apr;3(2):94-106.

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

Reza Azmi  1 , Boshra Pishgoo, Narges Norozi, Samira Yeganeh
Affiliations

Ensemble Semi-supervised Frame-work for Brain Magnetic Resonance Imaging Tissue Segmentation

Reza Azmi et al. J Med Signals Sens. 2013 Apr.

Abstract

Brain magnetic resonance images (MRIs) tissue segmentation is one of the most important parts of the clinical diagnostic tools. Pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. Supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to obtain. Moreover, they cannot use unlabeled data to train classifiers. On the other hand, unsupervised segmentation methods have no prior knowledge and lead to low level of performance. However, semi-supervised learning which uses a few labeled data together with a large amount of unlabeled data causes higher accuracy with less trouble. In this paper, we propose an ensemble semi-supervised frame-work for segmenting of brain magnetic resonance imaging (MRI) tissues that it has been used results of several semi-supervised classifiers simultaneously. Selecting appropriate classifiers has a significant role in the performance of this frame-work. Hence, in this paper, we present two semi-supervised algorithms expectation filtering maximization and MCo_Training that are improved versions of semi-supervised methods expectation maximization and Co_Training and increase segmentation accuracy. Afterward, we use these improved classifiers together with graph-based semi-supervised classifier as components of the ensemble frame-work. Experimental results show that performance of segmentation in this approach is higher than both supervised methods and the individual semi-supervised classifiers.

Keywords: Brain magnetic resonance image tissue segmentation; MCo_Training classifier; ensemble semi-supervised frame-work; expectation filtering maximization classifier.

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Conflict of interest statement

Conflict of Interest: None declared

Figures

Figure 1
Figure 1
An overall view of ensemble semi-supervised frame-work. White arrow shows training and gray arrows show test image segmentation process
Figure 2
Figure 2
Illustration of ensemble semi-supervised framework. White arrows show training and gray arrows show test image segmentation process
Figure 3
Figure 3
Segmentation results for supervised methods: Support vector machine, K-nearest neighbors and Naïve Bayesian
Figure 4
Figure 4
Segmentation results for semi-supervised methods: Co_training, MCo_Training, expectation maximization, expectation filtering maximization and graph-based method
Figure 5
Figure 5
Segmentation results for individual semi-supervised methods and semi-supervised ensemble frame-work
Figure 6
Figure 6
Illustration of energy levels for results of individual semi-supervised methods and semi-supervised ensemble frame-work
See this image and copyright information in PMC

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