Rough set theory and feed forward neural network based brain tumor detection in magnetic resonance images

Segmentation of images holds an important position in the area of image processing. Computer aided detection of abnormality in medical images is primarily motivated by the necessity of achieving maximum possible accuracy. There are lots of methods for automatic and semi- automatic image classification, though most of them fail because of unknown noise, poor image contrast, inhomogeneity and boundaries that are usual in medical images. The MRI (Magnetic resonance Imaging) brain tumor segmentation is a complicated task due to the variance and intricacy of tumors. The principle aim of the project is to perform the MRI Brain image classification of cancer, based on Rough Set Theory and Feed Forward Neural Network classifier. For this purpose, first the features are extracted from the input MRI images using Rough set theory, and then the selected features are given as input to Feed Forward Neural Network classifier. Finally, Feed Forward Neural Network classifier is utilized to perform two functions. The first is to differentiate between normal and abnormal. The second function is to classify the type of abnormality in benign or malignant tumor.