16 April 2012 IntegriSense molecular image sequence classification using Gaussian mixture model
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Abstract
Targeted fluorescence imaging agents such as IntegriSense 680 can be used to label integrin αvβ3 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.
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Tiancheng He, Tiancheng He, Zhong Xue, Zhong Xue, Kongkuo Lu, Kongkuo Lu, Miguel Valdivia y Alvarado, Miguel Valdivia y Alvarado, Stephen T. Wong, Stephen T. Wong, "IntegriSense molecular image sequence classification using Gaussian mixture model", Proc. SPIE 8317, Medical Imaging 2012: Biomedical Applications in Molecular, Structural, and Functional Imaging, 831722 (16 April 2012); doi: 10.1117/12.910832; https://doi.org/10.1117/12.910832
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