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Improving Brain Tumor Diagnosis Using MRI Segmentation Based on Collaboration of Beta Mixture Model and Learning Automata

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A Correction to this article was published on 24 April 2019

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Abstract

In this paper, an automatic brain tumor diagnosis system is presented using a new threshold-based segmentation method. The proposed segmentation method is based on collaboration of beta mixture model and learning automata (LA). This segmentation method approximates histogram of a given MR image through mixture of Beta functions whose optimized parameters are determined through LA tool. Each Beta function shows one pixel class, and the threshold values which used for segmentation are obtained via intersection of two adjacent Beta functions. Feature extraction is based on statistical features. Moreover, support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT) are applied as binary classifiers. In order to evaluate the performance, the proposed method is analyzed on set of 79 MR images from TCIA dataset and Harvard Medical School. The results of the experiments show that the proposed segmentation method presents superior average values for image similarity indices Dice and Jaccard. Moreover, the best accuracy of the presented brain tumor diagnosis system is obtained using SVM classifier with linear kernel, which is more than 98% in 10-fold cross-validation.

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  • 24 April 2019

    In the original publication, the affiliation of the author was published with typo error.

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Authors and Affiliations

  1. Department of Computer Engineering, Dezfoul Branch, Islamic Azad University, Dezfoul, Iran

    Akram Edalati-rad & Mohammad Mosleh

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  1. Akram Edalati-rad
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  2. Mohammad Mosleh
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Correspondence to Mohammad Mosleh.

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Edalati-rad, A., Mosleh, M. Improving Brain Tumor Diagnosis Using MRI Segmentation Based on Collaboration of Beta Mixture Model and Learning Automata. Arab J Sci Eng 44, 2945–2957 (2019). https://doi.org/10.1007/s13369-018-3320-1

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