Application of Fusion Technique with ImageJ Stacks Feature for Brain Tumor MRI Image Optimization

Abstract

Fusion techniques  on MRI for brain tumors can provide comprehensive visualization by combining Axial T2-Flair and Axial T1-GD (T1-weighted post-contrast) sequence images. Fusion MRI in brain tumors is able to clearly display the location, size and characteristics of the tumor. However, not all institutions can install such additional fusion software due to significant additional costs. Therefore, this study aims to prove that the Stacks feature on ImageJ as an alternative can be optimal in visualizing brain tumor image information through MRI fusion techniques. This study used 17 image samples with a quasi experimental design post test only without control group design to compare three analysis methods, namely fusion maximum intensity, minimum intensity and average intensity so that the most suitable projection can be determined. The evaluation of image quality was carried out through a histogram which was then analyzed with a crucal-wallis and the Mann Whitney u test, while the analysis of pathological information used a crucal-wallis, followed by a post hoc test  and continued with Mann Whitney u for further analysis. The results show that the stacks feature on ImageJ can be used in the application of fusion  techniques so that it will improve the contrast and sharpness of MRI images, especially in areas with high tumor activity. MRI images of brain tumors with  maximum fusion intensity produced images with  the highest average gray level and the best pathological information. This projection is more optimal than the minimum intensity and average intensity because it provides a more detailed and clear visualization of brain tumors.