DECODE-3DViz: Efficient WebGL-Based High-Fidelity Visualization of Large-Scale Images using Level of Detail and Data Chunk Streaming.

ABSTRACT: The DECODE-3DViz pipeline represents a major advancement in the web-based visualization of large-scale medical imaging data, particularly for peripheral artery computed tomography images. This research addresses the critical challenges of rendering high-resolution volumetric datasets via WebGL technology. By integrating progressive chunk streaming and level of detail (LOD) algorithms, DECODE-3DViz optimizes the rendering process for real-time interaction and high-fidelity visualization. The system efficiently manages WebGL texture size constraints and browser memory limitations, ensuring smooth performance even with extensive datasets. A comparative evaluation against state-of-the-art visualization tools demonstrates DECODE-3DViz's superior performance, achieving up to a 98% reduction in r

SUBMITTER: AboArab MA 

PROVIDER: S-EPMC12701164 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature