Video endoscopy represents a major advance in the investigation of gastrointestinal diseases. Reviewing endoscopy videos often involves frequent adjustments and reorientations to piece together a complete view, which can be both time-consuming and prone to errors. Image stitching techniques address this issue by providing a continuous and complete visualization of the examined area. However, endoscopic images, particularly those of the esophagus, present unique challenges. The smooth surface, lack of distinct feature points, and non-horizontal orientation complicate the stitching process, rendering traditional feature-based methods often ineffective for these types of images. In this paper, we propose a novel preprocessing pipeline designed to enhance endoscopic image stitching through advanced computational techniques. Our approach converts endoscopic video data into continuous 2D images by following four key steps: (1) keyframe selection, (2) image rotation adjustment to correct distortions, (3) surface unwrapping using polar coordinate transformation to generate a flat image, and (4) feature point matching enhanced by Adaptive Histogram Equalization for improved feature detection. We evaluate stitching quality through the assessment of valid feature point match pairs. Experiments conducted on 20 pediatric endoscopy videos demonstrate that our method significantly improves image alignment and stitching quality compared to traditional techniques, laying a robust foundation for more effective panoramic image creation.

翻译：视频内窥镜检查是胃肠道疾病研究领域的一项重大进展。回顾内窥镜视频通常需要频繁调整和重新定向以拼凑完整视图，这一过程既耗时又容易出错。图像拼接技术通过提供被检查区域的连续完整可视化来解决此问题。然而，内窥镜图像，尤其是食管图像，存在独特的挑战。其表面光滑、缺乏明显特征点以及非水平方向等特点使拼接过程复杂化，导致传统基于特征的方法对此类图像往往效果不佳。本文提出了一种新颖的预处理流程，旨在通过先进的计算技术增强内窥镜图像拼接效果。我们的方法通过四个关键步骤将内窥镜视频数据转换为连续二维图像：(1)关键帧选择，(2)通过图像旋转调整校正畸变，(3)采用极坐标变换进行表面展开以生成平面图像，(4)通过自适应直方图均衡化增强特征点匹配以改进特征检测。我们通过评估有效特征点匹配对来量化拼接质量。在20例儿科内窥镜视频上进行的实验表明，与传统技术相比，我们的方法显著提升了图像对齐与拼接质量，为创建更有效的全景图像奠定了坚实基础。