## 3 CAD Systems of Brain Disorders Based on Multimodal Fusion
### Multimodal Fusion Techniques Proposed in Literature: Description, Advantages and Disadvantages
#### 3.2.1 Spatial Domain Techniques  
* **Principal components analysis**: PCA used in many works [216; 217; 218; 219; 220; 221; 222; 223] makes it possible to carry out a linear orthogonal transformation of a multivariate set of data which contains variables correlated with N dimensions in other containing new variables not correlated to M smaller size dimensions. The transformation parameters sought are obtained by minimizing the error covariance introduced by neglecting N-M of the transformed components. This technique is very simple and effective. It benefits from faster processing time with high spatial quality. It selects the optimal weighting coefficients according to the content of the information; it removes the redundancy present in the input image. It compresses a large amount of input data without much loss of information. However, a strong correlation between the input images and the merged image is necessary. In addition, the merged image quality is generally poor with spectral degradation and color distortion. * **Hue-intensity-saturation**: HIS used in many works [224; 225; 226; 227; 228] converts a color image of the RGB space (red, green and blue) into an HIS color space. The intensity band (I) in the HIS space is replaced by a high-resolution panoramic image, then reconverted in the original RGB space at the same time as the previous hue band (H) and the saturation band (S), which creates an HIS fusion image. It is very simple, efficient in calculation and the processing time is faster. It provides high spatial quality and better visual effect. The change in intensity has little effect on the spectral information and is easy to manage. However, it suffers from artifact and noise which tend to weaken the contrast. It only processes multi-spectral bands and results in color distortion. * **Brovery transformation:** It is a combination of arithmetic operations which normalize the spectral bands before they are multiplied by the panoramic image. It retains the corresponding spectral characteristic of each pixel and transforms all the luminance information into a high-resolution panoramic image. This technique proposed by many works [229; 230; 231] is very simple, effective on the computer level and has a faster processing time. It produces RGB images with a high degree of contrast. Good for multi-sensory images and provides a superior, high-resolution visual image. Generally the Bovery fusion image is used as a reference for comparison with other fusion techniques. However, it ignores the requirement for high-quality synthesis of spectral information and causes spectral distortion, which results in color distortion and high-contrast pixel values in the input image. It does not guarantee to have clear objects of all the images. * **Guided filtering:** This technique [232; 233; 234; 235] is based on a local linear model which takes into account the statistics of a region in the corresponding spatial neighborhood in the guide image while calculating the value of the output pixel. The process first uses a median filter to obtain the two-scale representations. Then the base and detail layers are merged using a weighted average method. It is very simple, in terms of computation and adaptable to real applications whose computational complexity is independent of the size of the filtering kernel. It has good smoothing properties preserving the edges and does not suffer from the gradient reversal artifacts observed when using a bilateral filter. It does not blur strong edges in the decomposition process. Despite the simplicity and effectiveness of this technique; however, the principal problem with the majority of guided filters is associated with ignorance of the structural inconsistency between the ground truth and target images such as color [234]. Moreover, the halos could represent an obstacle [235].