Brain Tumors Detection on MRI Images through Extracting HOG Features

Authors

  • Seyed Enayatallah Alavi Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
  • Ehsan zare
  • Mohammad javad Rashti Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

DOI:

https://doi.org/10.46947/joaasr21201896

Keywords:

Tumor detection, Histograms of Oriented Gradients, Feature extraction, Support Vector Machine

Abstract

In this paper, a system of Computer Aided Diagnosis (CAD) is introduced for detecting brain tumors through MRI images. The proposed system consists of three main sections: segmentation, feature extraction, and categorization. In the segmentation phase, a system called Seeded Region Growing (SRG) is applied to separate brain tissue from other regions. In the feature extraction phase, the Histograms of Oriented Gradients (HOG) algorithm is used. Finally, in order to describe the images of the brain, we use Support Vector Machine (SVM) and classify the images in two tumor-free and tumor-grade groups, and then compare them with similar tasks. The results show a high efficiency of this approach compared to other methods. 850 MRI images have been applied to test and teach samples; the number of healthy brains is 300 and the number of defective brains is 550, and the system has achieved a precision rate of 93.2% in the categorization. 

Metrics

Metrics Loading ...

Downloads

Published

2018-06-30

How to Cite

Seyed Enayatallah Alavi, Ehsan zare, & Mohammad javad Rashti. (2018). Brain Tumors Detection on MRI Images through Extracting HOG Features. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 2(1), 9–25. https://doi.org/10.46947/joaasr21201896