## 2 Cad Systems of Brain Disorders Based on MRI Technology
### Segmentation of Brain Regions
#### 2.2.2 Segmentation Techniques Proposed in Literature: Description, Advantages and Disadvantages
It is based on the theory of fuzzy subsets giving rise to the concept of partial adhesion based on the membership functions. It is widely used in the segmentation and diagnosis of medical images. It provides better results for overlapping data. Unlike \(k\)-means where data must systematically belong to a single cluster, FCM assigns a fuzzy degree of belonging to each cluster for each data, which allows it to belong to several clusters. However, the computation time is considerable. It does not often provide standard results due to the randomness of the initial membership values. In addition, MRI images often contain a significant amount of noise, resulting in serious inaccuracies in segmentation. It only takes into account the intensity of the image, which causes unsatisfactory results for noisy images. The counterintuitive form of class membership functions limits its use. * **Mean shift:** it's an unsupervised deterministic method [62; 65; 66; 67], which makes it possible to locate the maxima--the modes--of a density function, from discrete data sampled through this function. It is based on a non-parametric algorithm which does not take any predefined form on the clusters and assumes no constraint on the number of clusters. Robust with outliers and able to manage arbitrary function spaces. However, it is sensitive to the selection of the window h which is not trivial. An inappropriate window size may result in the merging of modes or the generation of additional \"shallow\" modes. Costly from a calculation point of view and does not adapt well to the size of the function space. * **Threshold-based techniques:** the easiest way is to convert a grayscale image to a binary using a threshold value [68]. Pixels lighter than the threshold are white pixels in the resulting image and darker pixels are black pixels. Several improvements have been reported in which the threshold is selected automatically. They are very useful for the linearization of images, an essential task for any type of segmentation. They work well for fairly noisy images. Do not require prior image information. They are useful if the brightness of objects differs significantly from the brightness of the background. Their speed of execution is quite fast with minimum IT complexity. However, do not work properly for all types of MRI brain images, due to the large variation between foreground intensities and background image intensities. The reason why, selecting the appropriate threshold value is a tedious task. In addition, their performance degrades for images without apparent peaks or with a wide and flat valley [69]. * **Region growing method:** it allows to group pixels together in a homogeneous region, including growing, dividing and merging regions. It correctly separates the regions with the same characteristics already defined, especially when the criteria for region homogeneity are easy to define [69; 70; 71; 72; 73]. However, it is very sensitive to noise. Costly in memory and sequential from the calculation point of view. In addition, it requires manually selecting an origin point and requires deleting all the pixels connected to the preliminary source by applying a predefined condition. * **Mixture of laws (Gaussian mixture models):** parametric probabilistic method which allows each observation to be assigned to the most probable class. The classes follow a probability distribution (law), normal in the case of Gaussian mixture models (GMM). GMMs [74; 75; 76; 77; 78] require few parameters estimated by a simple likelihood function. These parameters can be estimated by adopting the EM algorithm in order to maximize the likelihood function of the _log_. However, GMM assume that each pixel is independent of its neighbors; this does not take into account the spatial relationships between neighboring pixels [79]. Also, the previous distribution does not depend on the pixel index. * **Markov Random field:** non parametric probabilistic method, which allows modeling the interactions between a voxel and its neighborhood.