# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{\(\sigma\)} & \multicolumn{2}{c}{**Initial**} & \multicolumn{2}{c}{**Switched Type Correction**} & \multicolumn{2}{c}{**Continuous Type Correction**} \\ \cline{3-8}  & & **PSNR (dB)** & **C (\%)** & **PSNR (dB)** & **C (\%)** & **PSNR (dB)** & **C (\%)** \\ \hline \multirow{2}{*}{_Lena_} & 0.05 & 26.00 & 63.94 & 30.19 & 66.38 & 30.26 & 66.07 \\ \cline{2-8}  & 0.1 & 20.06 & 48.10 & 25.47 & 48.55 & 26.58 & 47.05 \\ \hline \multirow{2}{*}{_Bridge_} & 0.05 & 26.03 & 62.96 & 27.54 & 62.74 & 27.59 & 61.67 \\ \cline{2-8}  & 0.1 & 20.19 & 47.44 & 23.17 & 46.32 & 23.52 & 44.30 \\ \hline \multirow{2}{*}{_House_} & 0.05 & 26.05 & 65.47 & 31.66 & 67.55 & 31.72 & 67.40 \\ \cline{2-8}  & 0.1 & 20.14 & 50.96 & 26.49 & 55.08 & 28.00 & 53.59 \\ \hline \multirow{2}{*}{_Camera_} & 0.05 & 26.24 & 66.29 & 30.73 & 65.94 & 30.82 & 65.72 \\ \cline{2-8}  & 0.1 & 20.45 & 51.59 & 25.86 & 51.28 & 26.70 & 50.49 \\ \hline \multirow{2}{*}{_Bout_} & 0.05 & 26.03 & 64.03 & 30.80 & 65.84 & 30.86 & 65.66 \\ \cline{2-8}  & 0.1 & 20.00 & 45.78 & 25.63 & 48.92 & 27.00 & 47.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between the correction methods based on the hierarchical correlation map. The test images are 256 × 256 pixels and have been degraded with white Gaussian additive noise. Figure 5: Images processed by the translation invariant approach of the proposed algorithm in the two cases considered. Sixteen circular trips were considered. (**a**) Image with white additive Gaussian noise. (**b**) Image obtained by mediating the images resulting from moving the input image and processing according to the first two stages of the proposed algorithm. (**c**) Image obtained by applying the third stage of the proposed algorithm (correction depending on the contour map) on the image from point b (final result case B). (**d**) Image obtained by integrally applying the invariant to translation of the proposed algorithm. To analyze the useful informational content in medical images, using hemodynamic modeling and imaging processing to improve diagnosis, we propose and will achieve the following objectives:  
* Improving image quality by selectively eliminating disturbing information, such as noise, and eliminating other defects caused by the acquisition device by using adaptive filters. * Highlighting areas of interest by adjusting light intensity and contrast and accentuating contours and textures. * Improving the ability to detect edges by using the image fusion method based on the wavelet algorithm. * Developing new methods for evaluating segmentation. * Evaluating the performance of edge detection methods using operators that analyze the structural similarity of images. * The use of high-performance algorithms of Artificial Intelligence to perform sub-pixelation calculations and the transition from macroscopic analysis currently used to microscopic analysis for the analysis of tumors generated by cancer by analyzing medical images. The innovative nature of the research will be based on the development of a high-performance algorithm to improve the results of medical image filtering using Gaussian functions, multidimensional wavelet decompositions and image fusion. We will also implement a high-performance algorithm by using a matched filtering that correlates a known signal, or a pattern, with an unknown signal to detect the presence of the pattern in the unknown signal, in order to improve the analyzed images. We will focus on using the method of merging wavelet images and neural networks, as it exceeds the limits of classical edge detection methods and provides a much more accurate map of decompositions at the sub-pixel level. Last but not least, we will develop an algorithm for the automatic selection of the detection threshold for the classification of brain medical images obtained from CT and MRI, using clustering algorithms.