Automatic Detection and Classification of Brain Tumor in Magnetic Resonance Images

Authors

  • Reza Hassanpour
  • Anwar A. Abufares
  • Gul Tokdemir

DOI:

https://doi.org/10.37591/jomtra.v5i2.1667

Abstract

Brain tumor is one of the serious diseases that have caused death to many people in recent years. The complex structure of the brain and its main function associated with the central nervous system and its critical role in controlling most of the functions of the body make detection of tumor a challenging task. Many techniques have been presented in the medical field in order to detect brain tumor from MRI images. In this study, a system is designed to detect the tumor in brain Magnetic Resonance Imaging (MRI) using a seed-based region growing algorithm (SBRG) in the segmentation process. After segmenting the region of interest (ROI) the texture features are extracted from the image and used to classify tumor and non-tumor tissues by the implementation of the Artificial Neural Network (ANN) classifier. The accuracy obtained by this system is 99.88%.

Keywords: Magnetic resonance imaging, gray level co-occurrence matrix, artificial neural networks, seed-based region growing

Cite this Article
Anwar A. Abufares, Reza Hassanpour, Gul Tokdemir. Automatic Detection and Classification of Brain Tumor in Magnetic Resonance Images. Journal of Multimedia Technology & Recent Advancements. 2018; 5(2): 34–40p.


Downloads

Published

2018-09-26

Issue

Vol. 5 No. 2 (2018)

Section

Research Articles

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.