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Brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. Automating this process is a challenging task due to the high diversity in appearance of tumor tissue among different patients and the ambiguous boundaries of lesions. In this paper, we propose a novel method of classification of primary brain tumor in MRI images using multi model texture features and kernel based support vector machine. The probabilities of each pixel that belongs to the foreground (tumor) and the background are estimated by global and custom classifiers that are trained through gray level co-occurrence and Texton co-occurrence matrix based feature vector, respectively. The proposed method is evaluated 80 T1 weighted brain MRI image sequences using the evaluation metrics such as sensitivity, specificity and accuracy. The classification results are compared with other neural network based classifiers such as RBF and FFNN. The accuracy level (94%) for our proposed approach is provided at detecting the tumors in the brain MRI images. The obtained results depict that the proposed brain tumor detection approach produces better results in terms of the evaluation metrics.

Robust Classification of Primary Brain Tumor in MRI Images based on Multi Model Textures Features and Kernel Based SVM