Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.

The artificial learning is a scientific discipline that covers many aspects of study in mathematics, statistics and algorithms. The techniques of artificial learning have been widely used in extracting knowledge from data, but we are now facing new challenges "complex data." The improvement of data mining techniques has become demanded. After a synthetic study of the different techniques of artificial learning and meta heuristics, this paper propose a hybrid solution "AntMeans" based on tools from data mining: kmeans and AntClust (algorithm based on artificial ants) then this solution is applied firstly in spatial data mining (the extraction of areas in a map of forest fires), and secondly in image data mining (characterization of image regions).