自动拼接食管内镜视频，解决视野窄与重复图案难题
Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching
- 融合LoFTR/SIFT/ORB特征，构建筛选池提升匹配精度
- 引入密度加权单应性优化，使拼接误差低、相似度高
- 适合临床内镜医生做食管病变连续观察，提升诊断效率
内镜检查是诊断胃肠道疾病的关键手段，但受限于视野狭窄及内部环境动态变化，尤其在食管区域，复杂且重复的结构使得图像拼接困难。本文提出一种专为内镜食管视频设计的自动图像展开与拼接框架。该方法结合LoFTR、SIFT和ORB等特征匹配算法，构建特征过滤池，并采用密度加权单应性优化（DWHO）算法提升拼接精度。通过融合连续帧，生成高分辨率全景视图，实现对食管黏膜的全面、准确视觉分析。实验表明，该框架在多段视频序列中均表现出低均方根误差（RMSE）和高结构相似性指数（SSIM），具备临床应用潜力，显著提升内镜视觉数据的质量与连贯性。
原文摘要 · Abstract (English)
Endoscopy is a crucial tool for diagnosing the gastrointestinal tract, but its effectiveness is often limited by a narrow field of view and the dynamic nature of the internal environment, especially in the esophagus, where complex and repetitive patterns make image stitching challenging. This paper introduces a novel automatic image unfolding and stitching framework tailored for esophageal videos captured during endoscopy. The method combines feature matching algorithms, including LoFTR, SIFT, and ORB, to create a feature filtering pool and employs a Density-Weighted Homography Optimization (DWHO) algorithm to enhance stitching accuracy. By merging consecutive frames, the framework generates a detailed panoramic view of the esophagus, enabling thorough and accurate visual analysis. Experimental results show the framework achieves low Root Mean Square Error (RMSE) and high Structural Similarity Index (SSIM) across extensive video sequences, demonstrating its potential for clinical use and improving the quality and continuity of endoscopic visual data.
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