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

Authors:
Mingxing Hu;Graeme Penney;Daniel Rueckert;Philip Edwards;Fernando Bello;Michael Figl;Roberto Casula;Yigang Cen;Jie Liu;Zhenjiang Miao;David Hawkes
Affiliations:
Centre for Medical Image Computing, University College London;Department of Imaging Sciences, King's College London;Department of Computing, Imperial College;Department of Surgical Oncology and Technology, Imperial College;Department of Surgical Oncology and Technology, Imperial College;Department of Computing, Imperial College;Cardiothoracic Surgery, St. Mary's Hospital, London, UK;Institute of Information Science, Beijing Jiaotong University, Beijing, China;Department of Biomedical Engineering, Beijing Jiaotong University, Beijing, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China;Centre for Medical Image Computing, University College London
Venue:
MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality
Year:
2010

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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.