Comparison of different spatial filters for identification of the gray matter in brain imaging

Objective: The study aims to compare different spatial filters for the identification of gray matter in brain images, with the goal of enhancing image contrast and improving interpretation in medical diagnostics. Materials: Ten spatial filters were selected, implemented, and evaluated using a graphical user interface (GUI) in Matlab. The filters ranged from grayscale conversions to more complex filters like Gaussian and sharpening filters. The effectiveness of each filter was measured through the correlation coefficient between pixel intensity curves generated by the filters and an ideal model curve. Results: The non-linear median spatial filter showed the highest correlation coefficient (0.8974), indicating that it closely approximates the ideal model curve for differentiating between white and gray matter. In contrast, the emboss filter displayed the lowest correlation coefficient (0.6972), suggesting it is not suitable for tissue identification. Conclusions: The median filter proved to be the most effective for identifying gray matter in brain images. The research suggests that combinations of different filters could further improve outcomes, and the developed GUI allows for thorough exploration of these combinations to optimize the filtering and analysis of medical images.