# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 2 Materials and Methods
### Visual Saliency Feature-Based Enhancement  
Visual saliency detection is used to make the region of our interest stand out more from its neighbors and surrounding pixels [28]. Some existing visual saliency detection methods highlight the edges and textures of the entire salient region. The saliency map can be obtained by computing the Euclidean distance in CIELab color space as follows:  
\[S(i,j)=\left\|I_{\mu}-I_{f}(i,j)\right\| \tag{11}\]  
where \(I_{\mu}\) is the average pixels of the input image in CIELab color space and \(S(i,j)\) is the computed significance value at \((i,j)\). \(I_{f}(i,j)\) is the corresponding pixel in the Gaussian blurred image of the input image, and \(\left\|\cdot\right\|\) denotes the \(L_{2}\) norm. However, these methods compute the difference between the average pixels of the entire input image and its corresponding blurred version and often fail to maintain good performance when the input image has a noisy background or the imaging scene is large. [PERSON] et al. [29] proposed a maximum symmetric surround-based saliency detection method. For an input image  
Figure 3: Illustration of SSLRA. (**a**) Source CBCT images. (**b**) Normalized curves of \(S(k)\). (**c**) Normalized curves of \(R(k)\). (**d**) Reconstructed images. (**e**) Difference maps between (**a**,**d**). with size \(m\times n\), the symmetric surround saliency value at the given pixel \(S_{SS}(i,j)\) can be computed as:  
\[S_{SS}(i,j)=\left\|I_{\mu}(i,j)-I_{f}(i,j)\right\| \tag{12}\]  
where \(I_{\mu}(i,j)\) is the average pixels of the sub-image centered at pixel \((i,j)\) in CIELab color space and computed as:  
\[I_{\mu}(i,j)=\frac{1}{A}\sum_{x=l-i_{o}}^{i+i_{o}}\sum_{y=l-j_{o}}^{j+j_{o}}I( x,y) \tag{13}\]  
where \(i_{o},j_{o}\) is the local window size, and the area \(A\) of the sub-image is determined by:  
\[A=(2i_{o}+1)(2j_{o}+1). \tag{14}\]  
When the visual saliency detection results are obtained by Equation (12), the salient region of the input image will be stretched and enhanced, as shown in Figure 4.  
In the proposed VSFE algorithm, the saliency detection result from Equation (12) will be used as the visual saliency decision map for further detail enhancement. The visual saliency enhancement can be computed by:  
\[I_{VSE}=I_{R}+\alpha S_{SS}(I_{R})\cdot I_{R} \tag{15}\]  
where \(\alpha\) is the enhancing weight parameter, \(I_{R}\) is the reconstructed result by the SSLRA, and \(S_{SS}(I_{R})\) is the visual saliency map of \(I_{R}\). Some examples of \(I_{VSE}\) are given in Figure 5c. Compared with the reconstructed results in Figure 5b, the contrast of the visual saliency enhancement images is higher and the important regions are easier to observe. It can be found that \(I_{VSE}\) only enhances the salient features, and in the next step, we perform the overall detail enhancement on the images to further improve their quality. To enhance detail, the mutually guided image filter (MuGIF) method is adopted in the proposed VSFE algorithm. The MuGIF can be used to extract the relative structure of two images and the structure information of a single image [30; 31; 32]. The related structure of \(\mathbf{T}\) and \(\mathbf{R}\) of MuGIF is defined as:  
\[\mathcal{R}(\mathbf{T},\mathbf{R})=\sum_{i}\sum_{d\in\{h,v\}}\frac{|\
abla_{ d}\mathbf{Ti}_{i}|}{|\
abla_{d}\mathbf{Ri}_{i}|} \tag{16}\]  
Figure 4: Examples of visual saliency detection. (**a**) Source CBCT images. (**b**) Pseudo-color image of (**a**). (**c**) Saliency map of (**a**). (**d**) Pseudo-color image of (**c**). where \(\mathcal{R}\)(**T**,**R**) is the structure discrepancy of **T** and **R**. In Equation (16), the penalty term \(|\
abla_{d}T_{i}|\) can be used to control the strength of the computed related structures. The solution of Equation (16) can be converted to:  
\[