Optimized Feature Parameter Extraction of Brain Tumor MRI

This study aimed to extract optimized feature parameters for PET-MRI fusion imaging, a state-of-the-art medical imaging technique that has been the focus of much recent research. The medical images obtained from this technique are characterized by high resolution and low exposure dose. A neural network was trained by inputting extracted feature values. As a result of the experiment, it was found that the R-value of the segmented image is closer to 1 than the original image. The reason is that the images obtained by segmenting the areas of the weighted parts already have similarities. Also, it was found that the similarity between T1 and T2 weighted images is high in the same combination, and the similarity is relatively low in different weighted images. The most important issue in medical imaging is ensuring the confidence of radiologists using artificial intelligence. To solve this problem, it is of utmost importance that the algorithm developer and radiological technologist work together to provide a solution that is integrated with the radiologist's workflow.