Advanced Endoscopy Imaging with Automatic Feedback

As we move towards a future where minimally invasive methods become the norm for surgeries and diagnostic procedures, it is increasingly vital to improve our strategies for viewing the organs and complex structures within our bodies. Image stitching presents an enticing solution, expanding our field of view by seamlessly weaving together a sequence of images. While existing stitching techniques do lean on the capabilities of endoscopy imaging, they, unfortunately, overlook the critical need for automated feedback when grappling with the complexities and challenges innate to endoscopy imaging. these methods struggle to stand firm against deformations and regions with low texture. In this paper, we introduce a robust endoscopic image-stitching algorithm designed to thrive in adversity. Its unique resilience to deformations and low-texture regions is reinforced by the inclusion of a radial basis function weighting that is paired harmoniously with location-dependent homography based on the corresponding locations of the strong features extracted by affine shape-adapted Hessian-Laplace detector. Crucially, this algorithm is steered by a sophisticated automatic feedback mechanism. This feedback system makes astute evaluations based on an image quality metric and the structural comparison between the sequences of endoscopy images. We have thoroughly validated the efficacy of our new approach using two public datasets, namely EndoSLAM and EndoAbS, under demanding conditions. The results eloquently illustrate the superior benefits of our technique. Our proposed method surpasses commonly employed techniques, delivering superior performance in quantitative metrics, including precision at 30.07%, recall at 114.89%, F1-score at 84.62%, and TRE at 46.07%.