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A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

Veeresh Ashok Mulimani, Sanjeev S. Sannakki, Vijay S. Rajpurohit
Copyright: © 2021 |Volume: 11 |Issue: 2 |Pages: 12
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781799862031|DOI: 10.4018/IJCVIP.2021040103
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MLA

Mulimani, Veeresh Ashok, et al. "A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images." IJCVIP vol.11, no.2 2021: pp.46-57. http://doi.org/10.4018/IJCVIP.2021040103

APA

Mulimani, V. A., Sannakki, S. S., & Rajpurohit, V. S. (2021). A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images. International Journal of Computer Vision and Image Processing (IJCVIP), 11(2), 46-57. http://doi.org/10.4018/IJCVIP.2021040103

Chicago

Mulimani, Veeresh Ashok, Sanjeev S. Sannakki, and Vijay S. Rajpurohit. "A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images," International Journal of Computer Vision and Image Processing (IJCVIP) 11, no.2: 46-57. http://doi.org/10.4018/IJCVIP.2021040103

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Abstract

MRI technique is widely used in the field of medicine because of its high spatial resolution, non-invasive characteristics, and soft tissue contrast. In this review article, a systematic study has been conducted to analyze the performance and issues of various techniques for brain tumor segmentation. Latest research on BTS in MRI with the higher resolution is utilized for the systematic review. The high-resolution images increase execution time of the classification, and accuracy is the other problem in BTS. Still, there is some research lacking in accuracy on the brain segmentation. Few researchers carried out the classification of different kinds of tissues in the brain images and also on the prediction on growth of tumor. Each method has specific technique to improve the performance of the BTS, and these methods are compared with one another in terms of result. Research comparison helps to understand the proposed method with their achieved results. Clustering algorithms such as K-means and FCM are generally used for segmentation, and GA, ANN, ANFIS, FCNN, SVM are commonly used as classifiers.

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