Abstract
This paper presents a neural network-based technique for automatic classification of magnetic resonance images (MRI) of the brain in two categories of benign, and malignant. The proposed method consists of five following stages; i.e., preprocessing, connected component labeling (CCL), fuzzy connectedness segmentation, feature extraction using DWT and classification using RBF and SVM respectively. Preprocessing involves removing low-frequency surroundings noise, normalize the intensity of the individual particles images, remove reflections, and masking portions of images. Anisotropic filter is used to remove the background noise and thus preserving the edge points in the image. In the third stage, once all groups dogged, each pixel is labeled according to the element to which it is assigned to. In the third stage, the fuzzy Connectedness segmentation is used for partitioning the image into meaningful regions. have been In the fourth stage, the obtained feature connected to MRI images using the discrete wavelet transform (DWT).In the classification stage, the RBF kernel and SVM is used to classify the subjects to normal or abnormal (benign, malignant) and Level set method is used for automatic detection and segmentation of Meningioma and glioma tumor. The proposed technique gives high-quality results for brain tissue detection and is more robust and efficient compared with other recent works.