HSI-Based Vector Graphics Algorithm for Enhanced Detection and 3D Visualization of Pulmonary Lesions on CT

Multidetector computed tomography (MDCT) enables high-resolution multiplanar and 3D reconstructions in thoracic imaging. However, conventional 3D reconstruction methods are computationally expensive, time-consuming, and require specialized expertise. This short communication introduces a novel algorithm (HGM) based on vector graphics and RGB-to-HSI (hue, saturation, intensity) color-space transformation, designed to provide a low-cost, high-precision tool for visualization and classification of intrathoracic lesions.

Chest CT DICOM datasets were processed using the HGM algorithm, developed in collaboration with visual-effects engineers. The pipeline incorporated RGB-to-HSI conversion, vector-graphics-based modeling, and scripted segmentation for automated isolation of thoracic organs and lesions. Reconstructions were rendered into an interactive format compatible with Adobe Animate®.

Pilot testing in a 41-year-old woman with an intrapulmonary teratoma demonstrated accurate 3D reconstruction for preoperative planning. Tumor dimensions were 14×12×10.3 cm, closely matching radiologic measurements (14×12×10 cm). The mean processing time was 15 minutes per dataset using modest computational resources (~US $1,600 workstation) and minimal training (~4 hours).

The HGM algorithm enabled rapid anatomical segmentation, real-time 3D manipulation, and dynamic visualization from multiple perspectives, offering an alternative to conventional 3D CT reconstruction methods.

RGB-to-HSI transformation provides low-cost, high-precision 3D images of intrathoracic lesions. Future directions will focus on validating the algorithm by testing its performance in different tumors, post-surgical follow-up, radiotherapy planning, and other applications.