A robust mosaicing method with super-resolution for optical medical images

Abstract

Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts. © 2010 Springer-Verlag.