Open Access

ARTICLE

HTM: A Hybrid Triangular Modeling Framework for Soft Tissue Feature Tracking

Lijuan Zhang¹, Yu Zhou², Jiawei Tian^3,*, Fupei Guo⁴, Xiang Zhang⁴, Bo Yang^4,*

1 School of Artificial Intelligence, Guangzhou Huashang College, Guangzhou, 511300, China
2 Research Institute of AI Convergence, Hanyang University ERICA, Ansan-Si, 15577, Republic of Korea
3 Department of Computer Science and Engineering, Hanyang University, Ansan-Si, 15577, Republic of Korea
4 School of Automation, University of Electronic Science and Technology of China, Chengdu, 610054, China

* Corresponding Authors: Jiawei Tian. Email: ; Bo Yang. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences 2025, 145(3), 3949-3968. https://doi.org/10.32604/cmes.2025.071869

Received 13 August 2025; Accepted 22 October 2025; Issue published 23 December 2025

Abstract

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.

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Keywords

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