Detection and Segmentation of Meningioma Brain Tumors in MRI brain Images using Curvelet Transform and ANFIS

Abstract

The detection of abnormal tumor region brain Magnetic Resonance Images (MRI) is complex task due to its similar structures between tumor and its surrounding regions. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classification method-based meningioma brain tumor detection is proposed. The proposed method consists of the following stages as preprocessing, transformation, feature extraction and classifications. The brain MR images are enhanced in preprocessing stage and this spatial domain image is converted into multi resolution image using Curvelet transform. The texture and statistical features are extracted from the transformed coefficients. These features are trained and classified by ANFIS classifier and further morphological operations are applied on the classified brain image to segment the tumor regions. This proposed meningioma tumor detection approach is analyzed in terms of sensitivity, specificity, Jaccard Similarity Index (JSI), Dice Similarity Index DSI) and accuracy. The reported results showed that an accuracy of 98.5%, sensitivity 91.5% and specificity 98.6 %was achieved from the finely Curvelet Transform and ANFIS Model.