Brain tumors, abnormal cells growing in the human brain,are common neurological diseases that are extremely harmful to human health. Malignant brain tumors can lead to high mortality. Magnetic resonance imaging (MRI)༌a typical noninvasive imaging technology, can produce high-quality brain images without damage and skull artifacts, as well as provide comprehensive information to facilitate the diagnosis and treatment of brain tumors. Additionally༌the segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation.
We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure. We focus on the effects of GLCM texture on brain tumor segmentation. First, 112 GLCM features for each voxel were extracted. Next, these features were ranked using the SVM-recursive feature elimination (SVM-RFE) method. Based on the sorting results, we found that when the number of features was 60, the value of the Dice similarity coefficient (DSC) tended to be flat. The GLCM texture features maximal correlation coefficient, information measure of correlation, Angular Second Moment, sum of squares, difference variance, contrast, and inverse difference moment were important for segmentation. Finally, we selected the top 60 grayscale features and constructed an ensemble SVM classifier to separate the abnormal mass of tissue from normal brain tissues.
The experimental material was a dataset called BraTs2015. The proposed model was verified with the Dice coefficient. For low-grade tumors, we obtained a 91.2% average Dice coefficient for segmenting the complete tumor region. For high-grade tumors, the average was slightly higher at 92.4%.
Our results demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.