TY - GEN
T1 - IntegriSense molecular image sequence classification using Gaussian mixture model
AU - He, Tiancheng
AU - Xue, Zhong
AU - Lu, Kongkuo
AU - Valdivia Y Alvarado, Miguel
AU - Wong, Stephen T.
PY - 2012
Y1 - 2012
N2 - Targeted fluorescence imaging agents such as IntegriSense 680 can be used to label integrin α vβ 3 expressed in tumor cells and to distinguish tumor from normal tissues. Coupled with endomicroscopy and image-guided intervention devices, fluorescence contrast captured from the fiber-optic imaging technique can be used in a Minimally Invasive Multimodality Image Guided (MIMIG) system for on-site peripheral lung cancer diagnosis. In this work, we propose an automatic quantification approach for IntegriSense-based fluorescence endomicroscopy image sequences. First, a sliding time-window is used to calculate the histogram of the frames at a given timepoint, also denoted as the IntegriSense signal. The intensity distributions of the endomicroscopy image sequences can be briefly classified into three groups: high, middle and low intensities, which might correspond to tumor, normal tissue, and background (air) tissues within the lungs, respectively. At a given time-point, the histogram calculated from the sliding time-window is fit with a Gaussian mixture model, and the average and standard deviation (std), as well as the weight of each Gaussian distribution can be identified. Finally, a threshold can be used to the weighting parameter of the high intensity group for tumor information detection. This algorithm can be used as an automatic tumor detection tool from IntegriSense-based endomicroscopy. In experiments, we validated the algorithm using 20 IntegriSense-based fluorescence endomicroscopy image sequences collected from 6 rabbit experiments, where VX2 tumor was implanted into the lung of each rabbit, and image-guided endomicroscopy was performed. The automatic classification results were compared with manual results, and high sensitivity and specificity were obtained.
AB - Targeted fluorescence imaging agents such as IntegriSense 680 can be used to label integrin α vβ 3 expressed in tumor cells and to distinguish tumor from normal tissues. Coupled with endomicroscopy and image-guided intervention devices, fluorescence contrast captured from the fiber-optic imaging technique can be used in a Minimally Invasive Multimodality Image Guided (MIMIG) system for on-site peripheral lung cancer diagnosis. In this work, we propose an automatic quantification approach for IntegriSense-based fluorescence endomicroscopy image sequences. First, a sliding time-window is used to calculate the histogram of the frames at a given timepoint, also denoted as the IntegriSense signal. The intensity distributions of the endomicroscopy image sequences can be briefly classified into three groups: high, middle and low intensities, which might correspond to tumor, normal tissue, and background (air) tissues within the lungs, respectively. At a given time-point, the histogram calculated from the sliding time-window is fit with a Gaussian mixture model, and the average and standard deviation (std), as well as the weight of each Gaussian distribution can be identified. Finally, a threshold can be used to the weighting parameter of the high intensity group for tumor information detection. This algorithm can be used as an automatic tumor detection tool from IntegriSense-based endomicroscopy. In experiments, we validated the algorithm using 20 IntegriSense-based fluorescence endomicroscopy image sequences collected from 6 rabbit experiments, where VX2 tumor was implanted into the lung of each rabbit, and image-guided endomicroscopy was performed. The automatic classification results were compared with manual results, and high sensitivity and specificity were obtained.
KW - Automatic classification
KW - Fluorescence microendoscope
KW - Gaussian model
KW - Integrisense
KW - Molecular imaging
UR - http://www.scopus.com/inward/record.url?scp=84860778462&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84860778462&partnerID=8YFLogxK
U2 - 10.1117/12.910832
DO - 10.1117/12.910832
M3 - Conference contribution
AN - SCOPUS:84860778462
SN - 9780819489661
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2012
T2 - Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging
Y2 - 5 February 2012 through 7 February 2012
ER -