Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features - Samadi Ghoushchi, Hamed; Pourasad, Yaghoub (International Journal of Online and Biomedical Engineering (iJOE), 2020)
The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T1W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic C-Means Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.