Detection and recognition of brain tumor based on DWT, PCA and ANN

Brain tumor is one of more dangerous diesis that affected more than 100 persons every day. The challenge is how to detect and recognise benign and malignant tumor without surgery. In this paper, initially, brain images are filtered to remove unwanted particles, then a new method for automatic segmentation of lesion area is carried out based on mean and standard deviation. Combining both solidity property and morphological operation used to detect only the tumor from segmented image. Mathematical morphology such as close used to join narrow breaks regions in an object, fill the small holes and remove small objects. Features extracted from image by using wavelet transform, followed by applying principle component analysis (PCA) to reduce the dimensions of features. Classification of tumor based on neural network, where the inputs to the network are thirteen statistical features and textural features. The algorithm is trained with 20 of brain MRI images and tested with 45 brain MRI images. Accuracy for this method was encourage and reach near 100% in identifying normal and abnormal tissues from MRI images.