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 |Pages: 12
DOI: 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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2. Methodology Followed In Brain Tumor Segmentation

One of the important processes in the field of medical image processing domain is the BTS. The Unsupervised segmentation of brain tumor was carried out in many researches to increase the efficiency of the images. The aim of this paper is to evaluate the working of the various methods of segmentation. This investigation is majorly focused on the clustering techniques and classifiers, which are commonly applied in this method. The general block diagram of BTS of the MRI images using clustering technique is shown in Figure 1.

Figure 1.

General Block diagram of BTS

IJCVIP.2021040103.f01

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