Image Segmentation Using 2D Discrete Wavelet Transform for Medical Image

Medical image segmentation is a critical step in various healthcare applications, aiding in diagnosis, treatment planning, and disease monitoring.In this study, we investigate the efficacy of a segmentation approach based on the 2D wavelet transform.Leveraging a dataset comprising 10 diverse medical images, we evaluate the performance of our segmentation method using three key metrics: accuracy, precision, and recall.Our findings demonstrate that the proposed approach enhances segmentation accuracy, offering promising results compared to existing methods.By harnessing the multi-resolution feature extraction capabilities of the 2D wavelet transform, our method achieves improved delineation of medical image structures, paving the way for more accurate and efficient healthcare interventions.