REVIEW OF BRAIN TUMOR DETECTION TECHNIQUES FROM MRI IMAGES

Abstract

In this paper, computer-based methods for defining tumor region in the brain using MRI images is presented. Brain tumor detection is a most important area in medical image processing. Various methods are used to know whether a brain having tumor or not. Magnetic resonance (MR) imaging is currently an indispensable diagnostic imaging technique in the study of the human brain . It is a non-invasive technique that provides fairly good contrast resolution for different tissues and generates an extensive information pool about the condition of the brain. Classification of human brain in magnetic resonance (MR) images is possible via supervised techniques such as artificial neural networks and support vector machine (SVM) , and unsupervised classification techniques unsupervised such as self organization map (SOM) and fuzzy c-means combined with feature extraction techniques. Other supervised classification techniques, such as k-nearest neighbors (k-NN) also group pixels based on their similarities in each feature image can be used to classify the normal/pathological T2-wieghted MRI images.