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HTM: A Hybrid Triangular Modeling Framework for Soft Tissue Feature Tracking
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AI
DOI：10.32604/cmes.2025.071869.png)
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Abstract
En 中文
In endoscopic surgery, the limited field of view and the nonlinear deformation of organs caused by patient movement and respiration significantly complicate the modeling and accurate tracking of soft tissue surfaces from endoscopic image sequences. To address these challenges, we propose a novel Hybrid Triangular Matching (HTM) modeling framework for soft tissue feature tracking. Specifically, HTM constructs a geometric model of the detected blobs on the soft tissue surface by applying the Watershed algorithm for blob detection and integrating the Delaunay triangulation with a newly designed triangle search segmentation algorithm. By leveraging barycentric coordinate theory, HTM rapidly and accurately establishes inter-frame correspondences within the triangulated model, enabling stable feature tracking without explicit markers or extensive training data. Experimental results on endoscopic sequences demonstrate that this model-based tracking approach achieves lower computational complexity, maintains robustness against tissue deformation, and provides a scalable geometric modeling method for real-time soft tissue tracking in surgical computer vision.
Keywords:
Hybrid triangular matching
HTM
medical surgery
soft tissue
feature tracking
geometric modeling
delaunay triangulation
barycentric coordinate system
Journal
C
IF:
2.5
Papers:
344
Citations:
0