## 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
The Structural Approach  
It takes into account the structural and contextual information of the image. * **Morphological gradient:** it's the difference between the operators of the gradient of expansion and erosion of an image [97; 98]. In this case, the value of a pixel corresponds to the intensity of the contrast in the nearest neighbor. Generally, the extensive and anti-extensive operators exploited by gradient masks are effective in determining the intensity transitions of gray levels in the borders of objects. However, this technique suffers from the problem of the edge detail smearing. In addition, its sensitivity in particular to white Gaussian noise condensed on the high-frequency part of the signal. * **Watershed line:** it interprets an image as a height profile, flooded from regional minima so that, the lines where the flooded areas touch represent the watersheds [99; 100]. It makes it possible to use the a priori knowledge of the clinician and his intervention, which facilitates visual evaluation in the higher level. However, it is difficult to implement and slow from a calculation point of view. In addition, the over-segmentation of images is generally frequent. The Form-Based Approach  
It searches for areas that derive from a given form a priori. * **Deformable models:** generate curves or surfaces (from a simple image), or hyper-surfaces (in the case of larger images). They use internal and external forces to delimit the limits of objects and thus distort images. We could distinguish parametric models (active contours or _snake_) [101; 102; 103; 104; 105] and geometric models [106]. They are robust to noise and parasitic edges thank to their ability to generate closed parametric surfaces or curves. Simple to implement on the continuum and achieve less than pixel accuracy, a property highly desirable for medical imaging applications. Ease of integrating border elements into a coherent mathematical description. They are able to enlarge or contract over time, within an image [52; 107]. However, they risk producing shapes whose topology is inconsistent with the real object, when applied to noisy images with ill-defined borders. * **Atlas:** allows an image segmented by an automated algorithm to correspond to a reference image (atlas) [108; 109]. These techniques take into account a priori knowledge of brain structures and manage segmentation as a recording problem. They are used in clinical practice, for computer-assisted diagnosis and offer a standard system for detecting properties and morphological differences between patients. They allow segmentation even if there is not a well-defined relationship between the intensities of the pixels and associated regions. However, building an atlas takes time. Difficult to produce objective validation, because segmentation is used when the information from the gray level intensities is not sufficient. * **Wavelets:** automatically extracts the histogram threshold from the image by wavelet transform. The threshold segmentation is carried out by exploiting multi-scale characteristics of the wavelet transformation [110; 111; 56; 112]. It preserves the sharpness of the contours and provides frequency information located on a function of a signal, which is beneficial for segmentation. However, the overall threshold value is not constant, which leads to a sensitivity of the transformation to the shift. A transformation of dimension greater than 1 suffers from a bad direction when the transformation coefficients reveal only a few orientations of characteristics, in the spatial domain. In addition, there is no information available on the phase of a signal or vector with complex values; it is calculated by applying real and imaginary projections. * **Spherical harmonics:** It offers solutions of the Laplace equation expressed in a spherical coordinate system [24; 113; 114; 115]. A base of orthogonal functions is created, which ensures the uniqueness of the decomposition of a form on the unit sphere.