Automatic Image Unfolding and Stitching Framework for Esophageal Lining Video Based on Density-Weighted Feature Matching

TL;DR

This paper presents an automatic framework for unfolding and stitching esophageal endoscopy videos, combining advanced feature matching and density-weighted optimization to produce accurate panoramic views for better clinical analysis.

Contribution

It introduces a novel combination of feature filtering and density-weighted homography optimization specifically designed for challenging esophageal video stitching.

Findings

Achieves low RMSE and high SSIM in extensive video sequences

Enhances the continuity and quality of endoscopic visual data

Demonstrates potential for clinical application

Abstract

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.