Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.
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.
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.
Dendritic spines are small, bulbous cellular compartments that carry synapses. Biologists have been studying
the biochemical and genetic pathways by examining the morphological changes of the dendritic spines at the
intracellular level. Automatic dendritic spine detection from high resolution microscopic images is an important
step for such morphological studies. In this paper, a novel approach to automated dendritic spine detection
is proposed based on a nonlinear degeneration model. Dendritic spines are recognized as small objects with
variable shapes attached to dendritic backbones. We explore the problem of dendritic spine detection from
a different angle, i.e., the nonlinear degeneration equation (NDE) is utilized to enhance the morphological
differences between the dendrite and spines. Using NDE, we simulated degeneration for dendritic spine detection.
Based on the morphological features, the shrinking rate on dendrite pixels is different from that on spines,
so that spines can be detected and segmented after degeneration simulation. Then, to separate spines into
different types, Gaussian curvatures were employed, and the biomimetic pattern recognition theory was applied
for spine classification. In the experiments, we compared quantitatively the spine detection accuracy with
previous methods, and the results showed the accuracy and superiority of our methods.
Video surveillance is to monitor the moving targets for detection, tracking and behavior analysis, but one of its problems
is the accuracy of detection. In this paper, an effective algorithm for moving targets detection and background separation
in video surveillance was proposed. We used the vector field based on neighborhood structure measurement to detect the
moving target and eliminate the impact of moving target detection caused by shadow. Meanwhile we compared the
traditional methods with our algorithm to illustrate the efficiency in detecting the whole moving targets in video.
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