Iterative Process Fusion Method in Uncovering Pathways of Vascular Structures Based on Hyperspectral Imaging

This study utilizes a hyperspectral imaging system for in vitro imaging of blood vessels. Image preprocessing techniques, including Savitzky-Golay filtering, median pooling, gamma correction, CLAHE, and bottom hat transformation, are utilized to smoothen the images and improve the contrast between blood vessels and skin. The study introduces an iterative process that integrates an Automatic Target Generation Procedure (ATGP) for blood vessel extraction, an Orthogonal Subspace Projection (OSP), and an Edge-Preserving Filter for target detection and boundary extraction. The process will visually demonstrate the pathways of the vascular structures from the images. The iterative process was applied to compare the original and enhanced images. The results showed that the original images' average IOU and Dice coefficients were 0.11 and 0.19, respectively, while the enhanced images showed improvements to 0.29 and 0.44, respectively. Furthermore, when evaluated using the skeleton similarity metrics, the curve similarity accuracy of the original and enhanced images was 0.71 and 0.89, respectively. These results confirm the significant effectiveness of the image enhancement and iterative process fusion method (IEPF) in improving vascular segmentation performance.