Enhanced Feature-based Image Stitching for Endoscopic Videos in Pediatric Eosinophilic Esophagitis
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
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.
