Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. Surgery is the primary treatment, and careful surgery can minimize recurrence odds. Magnetic Resonance Imaging (MRI) imaging with Magnetic Resonance Spectroscopy (MRS) is used to diagnose various types of tumors in the Central Nervous System (CNS). In this study, several classification methods were used to separate tumor and healthy tissue. Materials and Methods: This study examined the MRI and MRS results of seven people enrolled in this study in 2018. The data was obtained with a prescription from a neurologist and neurosurgeon. Choline (Cho) and N-Acetylaspartate (NAA) metabolite signals were selected as the reference signal after preprocessing and removing the water signal. With the support of 3 radiologists, each tumor and healthy vesicles were identified for every patient. Then, tumor and healthy voxels were separated based on Multilayer Perceptron (MLP), linear Support Vector Machine (SVM), Gaussian SVM, and Fuzzy system using the obtained values and four different methods. Results: Data extracted from Cho and NAA metabolites were fed into MLP, linear SVM, Gaussian and Fuzzy SVM as input, and the amounts of accuracy, sensitivity, and specificity were determined for each method. The maximum accuracy for training mode and test mode was equal to 89.7% and 87%, respectively, specific to classification using Gaussian SVM. The results also showed that the classification accuracy can be significantly increased by increasing the number of fuzzy membership functions from 2 to 6. Conclusion: The results of this study suggested that a more complex classification system, such as SVM with a Gaussian kernel and fuzzy system can be more efficient and reliable when it comes to separating tumor tissue from healthy tissues from MRS data.

Background: Magnetic resonance (MR) image is one of the most important diagnostic tools for brain tumor detection. Segmentation of glioma tumor region in brain MR images is challenging in medical image processing problems. Precise and reliable segmentation algorithms can be significantly helpful in the diagnosis and treatment planning. Methods: In this article, a novel brain tumor segmentation method is introduced as a postsegmentation module, which uses the primary segmentation method's output as input and makes the segmentation performance values better. This approach is a combination of fuzzy logic and cellular automata (CA). Results: The BraTS online dataset has been used for implementing the proposed method. In the first step, the intensity of each pixel is fed to a fuzzy system to label each pixel, and at the second step, the label of each pixel is fed to a fuzzy CA to make the performance of segmentation better. This step repeated while the performance saturated. The accuracy of the first step was 85.8%, but the accuracy of segmentation after using fuzzy CA was obtained to 99.8%. Conclusion: The practical results have shown that our proposed method could improve the brain tumor segmentation in MR images significantly in comparison with other approaches.

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