2D Reconstruction of Small Intestine's Interior Wall

TL;DR

This paper introduces a new wireless endoscopic image stitching method that enhances accuracy and efficiency by combining PCA-SIFT keypoint extraction, MLESAC outlier removal, and an optimized NMI-based registration approach, tested on real and simulated datasets.

Contribution

The paper presents a novel image stitching technique specifically designed for wireless endoscopic images, improving registration accuracy and computational speed.

Findings

Demonstrated high accuracy and robustness visually and quantitatively

Effective on real wireless endoscopic images and Micro-Ball system images

Improved registration speed with multiscale NMI approach

Abstract

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.