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Supervised segmentation of MRI brain images using combination of multiple classifiers

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Abstract

Segmentation of different tissues is one of the initial and most critical tasks in different aspects of medical image processing. Manual segmentation of brain images resulted from magnetic resonance imaging is time consuming, so automatic image segmentation is widely used in this area. Ensemble based algorithms are very reliable and generalized methods for classification. In this paper, a supervised method named dynamic classifier selection-dynamic local training local tanimoto index, which is a member of combination of multiple classifiers (CMCs) methods is proposed. The proposed method uses dynamic local training sets instead of a full statics one and also it change the classifier rank criterion properly for brain tissue classification. Selection policy for combining the different decisions is implemented here and the K-nearest neighbor algorithm is used to find the best local classifier. Experimental results show that the proposed method can classify the real datasets of the internet brain segmentation repository better than all single classifiers in ensemble and produces significantly improvement on other CMCs methods.

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Authors and Affiliations

  1. School of Computer Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

    Ali Ahmadvand & Mohammad Sharififar

  2. Biomedical Engineering Department, Faculty of Electrical Engineering, Iran University of Science and Technology (IUST), Narmak, 16846-13114, Tehran, Iran

    Mohammad Reza Daliri

Authors
  1. Ali Ahmadvand
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  2. Mohammad Sharififar
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  3. Mohammad Reza Daliri
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Correspondence to Mohammad Reza Daliri.

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Ahmadvand, A., Sharififar, M. & Daliri, M.R. Supervised segmentation of MRI brain images using combination of multiple classifiers. Australas Phys Eng Sci Med 38, 241–253 (2015). https://doi.org/10.1007/s13246-015-0352-7

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  • DOI: https://doi.org/10.1007/s13246-015-0352-7

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