Introduction:
As known, the brain tumor is one of the major causes for the increase in mortality among children and adults. A tumor is any mass that results from abnormal growths of cells in the brain. It may affect any person at almost any age. Brain tumor effects may not be the same for each person. Tumors can directly destroy healthy brain cells. They can also indirectly damage healthy cells by crowding other parts of the brain and causing inflammation, brain swelling, and pressure within the skull.
Brain tumors are either malignant or benign. A malignant tumor, also called brain cancer, grows rapidly and often invades or crowds healthy areas of the brain. Benign brain tumors do not contain cancer cells and are usually slow growing.
Also, widespread and universal use of computer technology in many life applications or fields such as medical decision support that covers a wide range of medical area, such as cancer research, heart diseases, gastroenterology, brain diseases. In the recent century, the Computer-Aided Diagnosis (CAD) is progressively becoming an essential area for intelligent systems [34]. The CAD becomes very important in many applications such as in detection or classification of disease.
Many procedure and diagnostic imaging techniques can be performed for the early detection of any abnormal changes in tissues and organs such as Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), X-ray, and Ultrasound. MRI is a primary medical imaging modality that is commonly used to visualize the structure and the function of human body. It provides rich information for excellent soft tissue contrast which is especially useful in neurological studies.
Medical image segmentation is a key step and a preliminary stage in computer aided. The success of medical image analysis depends heavily on accurate image segmentation algorithms, so an accurate segmentation of medical image is primary in radiotherapy planning, clinical diagnosis and treatment planning.
Image segmentation refers to a process of assigning labels to a set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. There are different techniques that would help solve the image segmentation. These techniques are categorized into many approaches such threes holding, contour, region, clustering and other optimization approaches using a Bayesian framework and neural networks.
Also, the texture analysis is an important task in many computer application of computer image analysis for classification, detection or segmentation images.
Texture extraction methods can be classified into three major categories: statistical, structural, and spectral. Texture measures used in this work based on statistical measures. However, the important process is classifying MRI into normal and abnormal classes based on pattern recognition concept which can be defined as a quantitative description of an object, while pattern class can be defined as a set of patterns that share some properties in common. For this goal researchers have proposed a lot of approaches which fall into two categories containing supervised classification techniques such as Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The other category has unsupervised classification techniques such as Self-Organization Map (SOM) and fuzzy c-means. On the other hand, supervised classifiers in the term of classification accuracy have a better performance than unsupervised classifiers.