## 2 Cad Systems of Brain Disorders Based on MRI Technology
### Analysis of Functional NeuroImages
#### 2.2.4 Critical Discussion about Segmentation Techniques  
Segmentation is a key step that determines the success or failure of the classification process in the CAD system. However, it represents an arduous process, due to the complexity of the medical data, the diversity of the artefacts in particular, the low signal/noise ratio, the uncertain limits of the images, the great variability of the tissues within the same population and the artifacts due to patient movement or little time for data acquisition. Despite decades of intensive research in the area of brain region segmentation, there is no reliable general theory for all types of images. That said, no standard method is established or considered effective. Therefore, a set of ad hoc methods has been devised by researchers that have received a certain degree of popularity. We can split these segmentation techniques into five categories: those based on shape, contour, region, graph theory and on the structural approach. Certain conclusions could be drawn concerning these methods. The neural model suffers from complexity from the point of view of the desired topology, it's appropriate learning and the generalization of the network. It would only be chosen if no prior distribution is required and no very high-quality object information is required. The MRF has been widely exploited due to its use of spectral, spatial, textural, contextual and earlier image properties. However, its implementation is very complex and it does not allow integration of form. The watershed model could however be applied to the segmentation of medical images, however, intensive research is needed to understand its mechanism. By making several models evolve simultaneously, the use of GA makes it possible to remedy the problem of local minima observed in deformable models, the estimation of the pose as well as the initialization of the model. The strong point of the clustering methods based on the theory of fuzzy subsets lies in the resolution of the ambiguities of the borders of the regions. In the framework of hybrid models they combine with neural networks, the MRF model and histogram thresholding method. The segmentation performance also depends on the homogeneity measures which must be taken into account in the analysis of complex regions. Several have been used in literature, spectral, spatial, texture, shape, scale, size, compactness and contextual, temporal and prior knowledge. Spectral measurement was the most primitive. However it is unable to process high-resolution imagery. Texture measurement was also widely used since it simultaneously benefits from spectral and spatial properties. However it often does not provide perfect segmentation. Therefore, to properly estimate the threshold of homogeneity or heterogeneity of the brain regions, it is beneficial to combine all or most of the measures for better segmentation results [138; 139; 140; 141]. For example, the interest of integrating prior knowledge and contextual information has opened up good tracks of research in the segmentation of medical images [47; 142]. In addition to this, the required information scale which is important for better segmentation performance unfortunately, it is selected manually in most existing works. In addition, it would be relevant to propose a quantitative analysis to the methods for assessing segmentation. Moreover, we noticed that some studies have proven the interest of applying hybrid models which integrate the advantages of several intelligent methods derived from soft computing in order to solve certain problems encountered in the brain regions segmentation, in particular the combination of fuzzy logic clustering algorithms, neural networks, stochastic models and bio-inspired optimization algorithms. The interest of this hybridization is revealed in the context where medical images are susceptible to different artifacts and noises which cause disconnected and indistinct limits.