A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance Images

Mohammed Sabbih Hamoud Al-Tamimi,Ghazali Sulong

doi:10.5539/mas.v9n8p354

Abstract

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). Standard Deviation, Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with 60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60% images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02% (training) and 98.19% (testing) are achieved.

Highlights

Magnetic Resonance Images (MRI) is employed in diverse medical fields, including heart diseases, cancer research and brain diseases (El-Dahshan et al, 2009)
The proposed method is tested for the standard dataset called Internet Brain Segmentation Repository (IBSR) which is accessible at the Center for Morphometric Analysis, Massachusetts General Hospital (USA) as shown in (Figure 10) (NITRC, 2011)
The feature vectors are comprised of approximate coefficients which are extracted from MR brain images and used as inputs for Support Vector Machine (SVM)

Summary

Introduction

MRI is employed in diverse medical fields, including heart diseases, cancer research and brain diseases (El-Dahshan et al, 2009). It creates high-quality two or three dimensional images of an object to accurately visualize and detect the brain tumors. MRI is the most common test for diagnosing and confirming the presence of brain tumor. It identifies the tumor location for recommended specialist treatment options (Horská & Barker 2010; Al-Tamimi & Sulong, 2014b; Al-Tamimi & Sulong, 2014a)

Methods

Results

Conclusion