## 2 Cad Systems of Brain Disorders Based on MRI Technology
### Segmentation of Brain Regions
#### 2.2.1 Related-Work to the Segmentation of Brain Regions  
In [18] the authors proposed the adaptive fuzzy C-means algorithm (AFCM) for the segmentation of multi-spectral MRI images in 2D and 3D. AFCM makes it possible to model the intensity inhomogeneity as a gain field by gradually varying the intensities in the image space. Performance comparisons were made with the fuzzy and noise-tolerant adaptive segmentation method (FANTASM), in a noisy environment. Misclassification rate and mean-squared error are used as evaluation measuring parameters. In [19], a modified FCM (MFCM) algorithm is proposed which incorporates both the local spatial context and the non-local information by using a new dissimilarity index instead of the usual distance metric. The efficiency of the algorithm is demonstrated by segmentation experiments and by comparison with other advanced algorithms, namely: standard FCM, spatial FCM (FCMS), FCM with spatial information (FCMSI) and fast generalized FCM (FGFCM). The measurement parameters used to quantitatively assess the performance are: Similarity index, false positive ratio and false negative ratio. Figure 1: Schematic diagram for magnetic resonance imaging (MRI) brain image interpretation (top) and overall block diagram of the corresponding computer-aided diagnosis (CAD) system (bottom). (A) Enhancement of brain signal and definition of regions of interests (ROIs). (B) Extraction of voxel features in a mathematical representation. (C) Reduction of voxel parameters and segmentation in brain regions. (D) Classification and categorization of patients into normal or abnormal classes. In [20], the authors proposed a method which makes it possible to divide the brain into homogeneous regions for the detection of tumors. The process was split into two stages: Pre-segmentation and segmentation and several techniques have been exploited such as anisotropic filtering and the stochastic model Markov random field (MRF). The maximum posterior criterion (MAP) was used to estimate the MRF achievement taking into account the observed dependent data. In [21] the authors applied the discriminant random fields (DRF) models for the segmentation of brain tumors. A comparison was made with the MRF models using the Jaccard similarity coefficient. In [22], the authors presented a method for the segmentation of WM tissue lesions (WML). Support vector machines (SVM) were used to integrate characteristics of 4 MRI acquisition protocols to distinguish WML from normal tissue. A visual assessment was performed using two experienced neuro-radiologists. A quantitative validation was also carried out with Pearson correlation, Spearman correlation, coefficient of variation and reliability coefficient. In [23], the authors proposed the adaptive mean shift (AMS) method to classify voxels in one of the GM, WM and CSF tissues. A comparison was made with the adaptive MAP (AMAP) and maximum posterior marginal-MAP (MPM-MAP) methods. Tanimoto coefficient was applied as an evaluation criterion. In [24], the authors used sets of contours of multiple sclerosis lesions (MS) taken from MRI segmented images, and united their 3D surfaces by spherical harmonics. The objective was the 3D reconstructions of MS lesions and calculates their volumes. A comparison was made with the slice stacking technique by applying quantitative measurement parameters such as misclassification rate, mean-squared error. In [25], the authors proposed a multi-context wavelet-based thresholding (MCWT) method to classify pixels with GM, WM and CSF tissues. A comparison was made with the wavelet and multigrid wavelet transforms. In [26], the authors proposed an algorithm based on the transformation into spherical wavelets. The algorithm is applied to the caudate nucleus and the hippocampus for the study of schizophrenia. The validation performed using the Average max error, average min error evaluation criteria showed efficiency from the calculation point of view and compared to the active shape model algorithm by capturing finer shape details.