MRI Image Segmentation-Based Sensitive Psychological Monitoring Signal Analysis

This study investigates the use of MRI image segmentation for sensitive signal analysis in the psychological monitoring. Pre-collected segmented MRI image templates are used for psychological brain mapping, along with the use of image classification methods to achieve the overall goal of psychological monitoring. The proposed methodology integrates a novel MRI image segmentation algorithm, EDSPSS, which employs atrous convolution and bilinear interpolation for efficient feature extraction and up-sampling. In addition, an image classification approach based on local feature enhancement using the EMD distance is introduced. Systematic verification demonstrates the effectiveness of the proposed methods through comprehensive performance evaluations, including segmentation accuracy, edge detection, region component accuracy, and classification accuracy. Systematic verification demonstrates the effectiveness of the proposed methods through comprehensive performance evaluations, including segmentation accuracy, edge detection, region component accuracy, and classification accuracy. The results show the superiority of the approach in both segmentation and classification tasks, demonstrating its potential for sensitive analysis of psychological surveillance signals.