26,056 research outputs found
Three-dimensional imaging with multiple degrees of freedom using data fusion
This paper presents an overview of research work
and some novel strategies and results on using data fusion in
3-D imaging when using multiple information sources. We examine
a variety of approaches and applications such as 3-D
imaging integrated with polarimetric and multispectral imaging,
low levels of photon flux for photon-counting 3-D imaging,
and image fusion in both multiwavelength 3-D digital holography
and 3-D integral imaging. Results demonstrate the benefits
data fusion provides for different purposes, including visualization
enhancement under different conditions, and 3-D reconstruction
quality improvement
A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces
Optical instruments are widely used for precision surface measurement. However, the dynamic range of optical instruments, in terms of measurement area and resolution, is limited by the characteristics of the imaging and the detection systems. If a large area with a high resolution is required, multiple measurements need to be conducted and the resulting datasets needs to be stitched together. Traditional stitching methods use six degrees of freedom for the registration of the overlapped regions, which can result in high computational complexity. Moreover, measurement error increases with increasing measurement data. In this paper, a stitching method, based on a Gaussian process, image registration and edge intensity data fusion, is presented. Firstly, the stitched datasets are modelled by using a Gaussian process so as to determine the mean of each stitched tile. Secondly, the datasets are projected to a base plane. In this way, the three-dimensional datasets are transformed to two-dimensional (2D) images. The images are registered by using an (x, y) translation to simplify the complexity. By using a high precision linear stage that is integral to the measurement instrument, the rotational error becomes insignificant and the cumulative rotational error can be eliminated. The translational error can be compensated by the image registration process. The z direction registration is performed by a least-squares error algorithm and the (x, y, z) translational information is determined. Finally, the overlapped regions of the measurement datasets are fused together by the edge intensity data fusion method. As a result, a large measurement area with a high resolution is obtained. A simulated and an actual measurement with a coherence scanning interferometer have been conducted to verify the proposed method. The stitching result shows that the proposed method is technically feasible for large area surface measurement
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Three-dimensional reconstruction of the tissue-specific multielemental distribution within Ceriodaphnia dubia via multimodal registration using laser ablation ICP-mass spectrometry and X-ray spectroscopic techniques
In this work, the three-dimensional elemental, distribution profile within the freshwater crustacean Ceriodaphnia dubia was constructed at a spatial resolution down to S mu m via a data, fusion approach employing state-of-the-art laser ablation inductively coupled plasma-time-of-flight mass spectrometry (LAICP-TOFMS) and laboratory-based absorption microcomputed tomography (mu-CT). C. dubia was exposed to elevated Cu, Ni, and Zn concentrations, chemically fixed, dehydrated, stained, and embedded, prior to mu-CT analysis. Subsequently, the sample was cut into 5 pm thin sections that were subjected to LA-ICPTOFMS imaging. Multimodal image registration was performed to spatially align the 2D LA-ICP-TOFMS images relative to the Corresponding slices of the 3D mu-CT reconstruction. Mass channels corresponding to the isotopes of a single element were merged to improve the signal-to-noise ratios within the elemental images. In order to aid the visual interpretation of the data, LA-ICP-TOEMS data wete projected onto the mu-CT voxels representing tissue. Additionally, the image resolution and elemental sensitivity were compared to those obtained with synchrotron radiation based 3D confocal mu-X-ray fluorescence imaging upon a chemically fixed and air-dried C. dubia specimen
Roadmap on optical security
Postprint (author's final draft
False discovery rate analysis of brain diffusion direction maps
Diffusion tensor imaging (DTI) is a novel modality of magnetic resonance
imaging that allows noninvasive mapping of the brain's white matter. A
particular map derived from DTI measurements is a map of water principal
diffusion directions, which are proxies for neural fiber directions. We
consider a study in which diffusion direction maps were acquired for two groups
of subjects. The objective of the analysis is to find regions of the brain in
which the corresponding diffusion directions differ between the groups. This is
attained by first computing a test statistic for the difference in direction at
every brain location using a Watson model for directional data. Interesting
locations are subsequently selected with control of the false discovery rate.
More accurate modeling of the null distribution is obtained using an empirical
null density based on the empirical distribution of the test statistics across
the brain. Further, substantial improvements in power are achieved by local
spatial averaging of the test statistic map. Although the focus is on one
particular study and imaging technology, the proposed inference methods can be
applied to other large scale simultaneous hypothesis testing problems with a
continuous underlying spatial structure.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS133 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
An open environment CT-US fusion for tissue segmentation during interventional guidance.
Therapeutic ultrasound (US) can be noninvasively focused to activate drugs, ablate tumors and deliver drugs beyond the blood brain barrier. However, well-controlled guidance of US therapy requires fusion with a navigational modality, such as magnetic resonance imaging (MRI) or X-ray computed tomography (CT). Here, we developed and validated tissue characterization using a fusion between US and CT. The performance of the CT/US fusion was quantified by the calibration error, target registration error and fiducial registration error. Met-1 tumors in the fat pads of 12 female FVB mice provided a model of developing breast cancer with which to evaluate CT-based tissue segmentation. Hounsfield units (HU) within the tumor and surrounding fat pad were quantified, validated with histology and segmented for parametric analysis (fat: -300 to 0 HU, protein-rich: 1 to 300 HU, and bone: HU>300). Our open source CT/US fusion system differentiated soft tissue, bone and fat with a spatial accuracy of ∼1 mm. Region of interest (ROI) analysis of the tumor and surrounding fat pad using a 1 mm(2) ROI resulted in mean HU of 68±44 within the tumor and -97±52 within the fat pad adjacent to the tumor (p<0.005). The tumor area measured by CT and histology was correlated (r(2) = 0.92), while the area designated as fat decreased with increasing tumor size (r(2) = 0.51). Analysis of CT and histology images of the tumor and surrounding fat pad revealed an average percentage of fat of 65.3% vs. 75.2%, 36.5% vs. 48.4%, and 31.6% vs. 38.5% for tumors <75 mm(3), 75-150 mm(3) and >150 mm(3), respectively. Further, CT mapped bone-soft tissue interfaces near the acoustic beam during real-time imaging. Combined CT/US is a feasible method for guiding interventions by tracking the acoustic focus within a pre-acquired CT image volume and characterizing tissues proximal to and surrounding the acoustic focus
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