  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.
