Automatic Detection and Classification of Brain Tumor in Magnetic Resonance Images

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.