Abstract: Considering that brain tumor is one of the diseases which threaten members of a society and unless it is not diagnosed at the right time it can lead to people’s death, its diagnosis is of too much importance. In most cases individual develops tumor lesion but since it is very small, it cannot be detected by first medical images such as CT and MRI and it may postpone diagnosis and may also lead to an irreparable lesion. During the past decade in order to help radiologists and specialized physicians, most experts have tended to pay more attention to computer algorithms for the diagnosis of this phenomenon. In this case they can use computer to analyze medical images taken from brain more precisely and tumor detection can be done. Using this method may lead to reduce the risk of tumor diagnosis. In this article we extract candidate abnormal areas by the use of morphological operations and then combination of artificial neural networks and fuzzy logic that refers to NeuroFuzzy is used to classify tumor region from non tumor candidate areas. After localization of the tumor region Whole brain tumor boundary was extracted by the use of traditional level set method. The evaluation result with brain MRI tumor images shows that our proposed method is more precise and robust for brain tumor segmentation.