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Segmentation and Classification of MRI Brain Tumor using FLGMM Segmentation and Neuro-Fuzzy Classifier

Segmentation and Detection of Brain tumor is very vital because it provides anatomical information of tissues which helps in treatment of patient. There are number of techniques for image segmentation. Efficient segmentation methods lead to accurate diagnosis. For early diagnosis it will make sense to combine segmentation, feature extraction and classification process into one model. Taking into account this, we are proposing an approach for brain tumor identification based on Fuzzy Local Gaussian Mixture Model segmentation method by assuming local data within pixels neighborhood satisfy Gaussian Mixture Model and grey level co-occurrence is used for feature extraction. Gray Level Co-occurrence Matrix (GLCM) characteristics features are used with the MR image for training of neural network then tumor is detected using neuro fuzzy classifier. Experimental results show promising results in terms of classification and segmentation accuracy.