Nonlinear motion compensation using cubature Kalman filter for in vivo fluorescence microendoscopy in peripheral lung cancer intervention

Fluorescence microendoscopy can potentially be a powerful modality in minimally invasive percutaneous intervention for cancer diagnosis because it has an exceptional ability to provide micron-scale resolution images in tissues inaccessible to traditional microscopy. After targeting the tumor with gu...

Full description

Saved in:
Bibliographic Details
Published in:Journal of biomedical optics 2013-01, Vol.18 (1), p.016008-016008
Main Authors: He, Tiancheng, Xue, Zhong, Alvarado, Miguel Valdivia y, Wong, Kelvin K, Xie, Weixin, Wong, Stephen T. C
Format: Article
Language:English
Subjects:
Algorithms
Animals
Cancer
Computer Simulation
Diagnosis
Endoscopy - methods
Fluorescence
Image Processing, Computer-Assisted - methods
Imaging
Lung Neoplasms - chemistry
Lung Neoplasms - pathology
Microscopy
Microscopy, Fluorescence - methods
Nonlinear Dynamics
Nonlinearity
Rabbits
Respiratory Mechanics - physiology
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Fluorescence microendoscopy can potentially be a powerful modality in minimally invasive percutaneous intervention for cancer diagnosis because it has an exceptional ability to provide micron-scale resolution images in tissues inaccessible to traditional microscopy. After targeting the tumor with guidance by macroscopic images such as computed tomorgraphy or magnetic resonance imaging, fluorescence microendoscopy can help select the biopsy spots or perform an on-site molecular imaging diagnosis. However, one challenge of this technique for percutaneous lung intervention is that the respiratory and hemokinesis motion often renders instability of the sequential image visualization and results in inaccurate quantitative measurement. Motion correction on such serial microscopy image sequences is, therefore, an important post-processing step. We propose a nonlinear motion compensation algorithm using a cubature Kalman filter (NMC-CKF) to correct these periodic spatial and intensity changes, and validate the algorithm using preclinical imaging experiments. The algorithm integrates a longitudinal nonlinear system model using the CKF in the serial image registration algorithm for robust estimation of the longitudinal movements. Experiments were carried out using simulated and real microendoscopy videos captured from the CellVizio 660 system in rabbit VX2 cancer intervention. The results show that the NMC-CKF algorithm yields more robust and accurate alignment results.
ISSN:1083-3668
1560-2281
DOI:10.1117/1.JBO.18.1.016008