# MRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review
## 3 Results
### Methods of Image Fusion
#### 3.1.2 Image Merging Methods
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Paper** & **Merging Category** & **Method** \\ \hline
[24] & MSD & Image fusion using Discrete Wavelet Transform (DWT) and Discrete Ripple Transform (DRT); \\ \hline
[25] & MSD & Image fusion using Non-Subsampled Shearlet Transform (NSST) \\ \hline
[26] & Non-MSD & Pulse-Coupled Neural Network (PCNN) \\ \hline
[27] & Hybrid & Multi-modal image fusion using WT and neuro-fuzzy \\ \hline
[28] & MSD & Fusion using Discrete Wavelet Transform (DWT) and Guider Filter \\  & & (GF) \\ \hline
[29] & MSD & Image fusion using Non-Subsampled Shearlet Transform (NSST) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Articles about multi-modal image merging. and used MATLAB to implement the algorithm. For the validation, ten pairs of grayscale and four pairs of color images were used. This method uses objective and subjective performance metrics to validate it against a non-denoising fusion method. [PERSON] et al. 's study demonstrated that denoising the image improves the fusion procedure at the cost of increased execution time. Another example of MSD, which solves some drawbacks of the DWT, is the proposed method by [PERSON] et al. [28]. [PERSON] et al. used a Guided Filter (GF), a technique that takes two inputs, an original image and a guided image, to obtain an output image with the information of the input image and some characteristics of the guided image. [PERSON] et al. used the DWT to decompose the image and used the GF to improve the weighted maps using the approximation coefficient from the inputs as the guided image. [PERSON] et al. validated the procedure with 15 pairs of CT and MRI images against two DWT methods; one using choose-max fusion and the second one using intuitionistic fuzzy inference fusion. The validation was performed using the metrics of the standard deviation, average gradient, and edge strength. The method of [PERSON] et al. showed better performance with all the images. However, the method requires further validation because the comparison was performed individually for every image pair, without using any statistical methodology for the selected image sample. Non-Multi-Scale Decomposition Methods  
The non-MSDs are the methods outside the MSD category. Some examples of these are the sub-space and the artificial neural network methods [30; 39]. The sub-space methods project a high-dimensional input image into a lower-dimensional space, achieving better efficiency due to the fact that the manipulation of lower-dimensional data requires less memory. A well-known example of sub-space techniques is the Principal Component Analysis (PCA) method [30]. The artificial network methods use a mathematical model to process information, which uses concepts inspired by biological neural networks, where the main advantage is the capability to predict, analyze, and infer the information of a dataset [39]. The Pulse-Coupled Neural Network (PCNN) is one of the most-used NNs, which was developed by [PERSON], based on the cat visual cortex [41; 42]. One example of the PCNN in image fusion is the technique developed by [PERSON] et al., who used PCNN with the quantum-behaved particle swarm optimization [26]. [PERSON] et al. validated the method against five different fusion techniques, using five pairs of MRI and CT images. The compared methods were: (i) Laplacian pyramid; (ii) the PCNN; (iii) the dual-channel PCNN; (iv) the PCNN with differential evolution algorithms; and (v) the dual-channel PCNN with PSO evolutionary learning. In the evaluation, the [PERSON] technique obtained better performance in the MI and Structural Similarity Index (SSIM), scoring 1.72 and 0.77, respectively. Lastly, some methods use a combination of MSD and non-MSD. These techniques usually have better performance in feature preservation, but have a drawback in the computing times. An example of a hybrid method is the approach of [PERSON] et al., who used the NSST with the PCNN to fuse MRI and CT images [43].