Brain tumors are part of a group of common, non-communicable, chronic and potentially lethal diseases affecting mostfamilies in Europe. Imaging plays a central role in brain tumor management, from detection and classification to staging andcomparison. <br/>Increasingly, magnetic resonance imaging (MRI) scan is being used for suspected brain tumors, because in addition tooutline the normal brain structures in great detail, has a high sensitivity for detecting the presence of, or changes within, a tumor.Currently most of the process related to brain tumors such as diagnosis, therapy, and surgery planning are based on its previoussegmentation from MRI. Brain tumor segmentation from MRI is a challenging task that involves various disciplines. The tumors to besegmented are anatomical structures, which are often non-rigid and complex in shape, vary greatly in size and position, and exhibitconsiderable variability from patient to patient. Moreover, the task of labeling brain tumors in MRI is highly time consuming and thereexists significant variation between the labels produced by different experts. <br/>The challenges associated with automated brain tumor segmentation have given rise to many different segmentationapproaches. Although the reported accuracy of the proposed methods is promising, these approaches have not gained wide acceptance among the neuroscientists for every day clinical practice. Two of the principal reasons are the lack of standardizedprocedures, and the deficiency of the existing methods to assist medical decision following a technician way of work. <br/>For a brain tumor segmentation system has acceptance among neuroscientists in clinical practice, it should supportmedical decision in a transparent and interpretable way emulating the role of a technician, considering his experience and knowledge. This includes knowledge of the expected appearance, location, variability of normal anatomy, bilateral symmetry, andknowledge about the expected intensities of different tissues. The image related problems and the variability in tissue distribution among individuals in the human population makes that some degree of uncertainty must be considered together with segmentationresults. <br/>A possible solution for designing complex systems, in which it is required to incorporate the experience of an expert, or the related concepts appear uncertain, is the use of soft computing techniques such as fuzzy systems. An important advantage of fuzzysystems is their ability for handling vague information. <br/>In this work, it is proposed the development of a method to assist the specialists in the process of segmenting braintumors. The main objective is to develop a system that can follow a technician way of work, considering his experience andknowledge. More concretely, it is presented a fully automatic and unsupervised segmentation method, which considers humanknowledge. The method successfully manages the ambiguity of MR image features being capable of describing knowledge about thetumors in vague terms. The method was developed making use of the powerful tools provided by fuzzy set theory. <br/>This thesis presents a step-by-step methodology for the automatic MRI brain tumor segmentation. For achieving the fullyautomatic and unsupervised segmentation, objective measures are delineated by means of adaptive histogram thresholds for defining the non-tumor and tumor populations. For defining the tumor population a symmetry analysis is conducted. <br/>The proposed approach introduces a new way to automatically define the membership functions from the histogram. The proposed membership functions are designed to adapt well to the MRI data and efficiently separate the populations. Since any post-processing is needed, and the unique pre-processing operation is the skull stripping, the proposed segmentation technique reduces the computational times. The proposed approach is quantitatively comparable to the most accurate existing methods, even thoughthe segmentation is done in 2D.
