## 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  
Despite decades of research, there is no standard method that could be considered effective for all types of medical images. However, a set of ad hoc methods have received a certain degree of popularity presented in Figure 2.  
Figure 2: Classification of the various MR brain image segmentation methods described in the literature. In this work, the methods were classified in five categories based on the approaches (in green color): Form, structural approaches, graph theory, region and contour. FCM: Fuzzy C-means, AFCM: Adaptive FCM, MFCM: Modified FCM, FCMS: FCM spatial, FCMSI: FCM with spatial information, FGFR: Fast generalized FCM, FANTASM: Fuzzy and noise tolerant adaptive segmentation method, PCM: Possibilistic C-means, BCFCM: Bias-corrected FCM, FFCM: Fuzzy PCM, GA: Genetic algorithm, PSO: Particle swarm optimization, MRF: Markov random field, DRFs: Discriminative random fields, HMM: Hidden Markov models, GMM: Gaussian mixture model, DNN: Deep neural network, CNN: Convolutional neural network, MLP: Multi-layer perceptron. The Region Approach  
It is designed to partition images into several classes. * **Artificial neural networks:** ANNs used in several works related to neuroimaging [38; 39; 40; 41; 42; 43; 44; 45; 46] are a supervised deterministic method, represented by an interconnected group of artificial neurons using a mathematical model to process information. It performs well in complex and multivariate nonlinear domains, such as tumor segmentation where it becomes difficult to use decision trees or rule-based systems. It also works a little better for noisy data. Data allocation is not required as in the case of statistical modeling. Its learning process consumes enough time, usually with gradient-type methods. The representation of knowledge is not explicit, in the form of rules or other easily interpretable. Initialization may affect the result which may cause overtraining. * **Genetic algorithms:** GAs exploited in many neuroimaging studies [47; 48; 49; 50; 51; 52] are a supervised deterministic method of optimizing research that exploits the concepts of natural selection. It differs from traditional optimization methods in four points: (1) It is a parallel search approach in a population of points, thus having the possibility of avoiding being trapped in a local optimal solution. (2) Its selection rules are probabilistic. (3) She works on the chromosome, a coded version of the potential solutions of the parameters, rather than the parameters themselves. (4) It uses the fitness score, obtained from objective functions, without any other derived or auxiliary information. However, its optimization process depends on the fitness function. It is hard to create good heuristics that really reflect our goal. It is difficult to select the initial parameters (the number of generations, the size of the population, etc.). * **\(k\)-means:**\(k\)-means [53; 54; 55; 56; 57] are a deterministic method based on unsupervised learning which makes it possible to divide a set of data into k clusters. It is widely used for brain segmentation with mainly satisfactory results, to overcome the isolated distribution of pixels inside the image segments. Its execution is simple to implement, fast in real time and in calculation even with a large number of variables. Unfortunately, the unstable quality of the results prevents its application in the case of automatic segmentation. Generally, a degradation of the quality of the segmentation is observed in the case of an automatic segmentation, or when the weight of the pixels in the neighboring local regions is added. Difficulty predicting the k value. Different initial partitions can result in different final clusters. The algorithm only works well when spherical clusters are naturally available in the original data. * **Fuzzy \(C\)-means:** FCM [19; 53; 58; 59; 60; 61; 62; 63; 64] is an unsupervised deterministic method which represents the advanced version of \(k\)-means.