Claudio Vinegoni, Ph.D. | Noise suppressed, multifocus image fusion for enhanced intraoperative navigation

J. Biophotonics
Noise suppressed, multifocus image fusion for enhanced intraoperative navigation

Fumene Feruglio^†, P., Vinegoni^#†, C., Fexon, L., Thurber, G., Sbarbati, A., and Weissleder, R.

Journal of Biophotonics 2013

ABS URL PMID DOI PMCID CITE HTML PDF Code

Current intraoperative imaging systems are typically not able to provide sharp’ images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix. ((c) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
@article{2013-BIOPHTNCS, author= {Fumene Feruglio<sup>†</sup>, P. and Vinegoni<sup>#†</sup>, C. and Fexon, L. and Thurber, G. and Sbarbati, A. and Weissleder, R.}, title= {Noise suppressed, multifocus image fusion for enhanced intraoperative navigation}, journal= {Journal of Biophotonics}, alternatejournal = {J. Biophotonics}, year = {2013}, volume = {6}, number = {4}, pages = {363-370}, issn = {1864-063X}, doi = {10.1002/jbio.201200086}, pmcid = {PMC3779878}, pmid= {22887724} }
22887724

PMC3779878

10.1002/jbio.201200086

Abstract

"Current intraoperative imaging systems are typically not able to provide sharp' images over entire large areas or entire organs. Distinct structures such as tissue margins or groups of malignant cells are therefore often difficult to detect, especially under low signal-to-noise-ratio conditions. In this report, we introduce a noise suppressed multifocus image fusion algorithm, that provides detailed reconstructions even when images are acquired under sub-optimal conditions, such is the case for real time fluorescence intraoperative surgery. The algorithm makes use of the Anscombe transform combined with a multi-level stationary wavelet transform with individual threshold-based shrinkage. While the imaging system is integrated with a respiratory monitor triggering system, it can be easily adapted to any commercial imaging system. The developed algorithm is made available as a plugin for Osirix. ((c) 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)"

Full citation

For attribution in academic contexts, please cite this work as:

Fumene Feruglio^†, P., Vinegoni^#†, C., Fexon, L., Thurber, G., Sbarbati, A., & Weissleder, R. (2013). Noise suppressed, multifocus image fusion for enhanced intraoperative navigation. Journal of Biophotonics, 6(4), 363–370. https://doi.org/10.1002/jbio.201200086