TY - GEN
T1 - A motion correction algorithm for microendoscope video computing in image-guided intervention
AU - He, Tiancheng
AU - Xue, Zhong
AU - Xie, Weixin
AU - Wong, Solomon
AU - Wong, Kelvin
AU - Alvarado, Miguel Valdivia Y
AU - Wong, Stephen T C
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - In multimodality image-guided intervention for cancer diagnosis, a needle with cannula is first punctured using CT or MRI -guided system to target the tumor, then microendoscopy can be performed using an optical fiber through the same cannula. With real-time optical imaging, the operator can directly determine the malignance of the tumor or perform fine needle aspiration biopsy for further diagnosis. During this operation, stable microendoscopy image series are needed to quantify the tissue properties, but they are often affected by respiratory and heart systole motion even when the interventional probe is held steadily. This paper proposes a microendoscopy motion correction (MMC) algorithm using normalized mutual information (NMI)-based registration and a nonlinear system to model the longitudinal global transformations. Cubature Kalman filter is thus used to solve the underlying longitudinal transformations, which yields more stable and robust motion estimation. After global motion correction, longitudinal deformations among the image sequences are calculated to further refine the local tissue motion. Experimental results showed that compared to global and deformable image registrations, MMC yields more accurate alignment results for both simulated and real data.
AB - In multimodality image-guided intervention for cancer diagnosis, a needle with cannula is first punctured using CT or MRI -guided system to target the tumor, then microendoscopy can be performed using an optical fiber through the same cannula. With real-time optical imaging, the operator can directly determine the malignance of the tumor or perform fine needle aspiration biopsy for further diagnosis. During this operation, stable microendoscopy image series are needed to quantify the tissue properties, but they are often affected by respiratory and heart systole motion even when the interventional probe is held steadily. This paper proposes a microendoscopy motion correction (MMC) algorithm using normalized mutual information (NMI)-based registration and a nonlinear system to model the longitudinal global transformations. Cubature Kalman filter is thus used to solve the underlying longitudinal transformations, which yields more stable and robust motion estimation. After global motion correction, longitudinal deformations among the image sequences are calculated to further refine the local tissue motion. Experimental results showed that compared to global and deformable image registrations, MMC yields more accurate alignment results for both simulated and real data.
KW - Cubature Kalman filter
KW - deformable video registration
KW - Fluorescence microendoscopy
KW - motion correction
KW - normalized mutual information
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U2 - 10.1007/978-3-642-15699-1_28
DO - 10.1007/978-3-642-15699-1_28
M3 - Conference contribution
AN - SCOPUS:78049448013
SN - 3642156983
SN - 9783642156984
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 267
EP - 275
BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Y2 - 19 September 2010 through 20 September 2010
ER -