The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Christian M Langton - One of the best experts on this subject based on the ideXlab platform.
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comparison of active set method deconvolution and Matched Filtering for derivation of an ultrasound transit time spectrum
Physics in Medicine and Biology, 2015Co-Authors: Marieluise Wille, Michael Zapf, Nicole V Ruiter, H Gemmeke, Christian M LangtonAbstract:The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a Matched-Filtering approach to the input and output signals. This study compares the Matched-Filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The Matched-Filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while Matched-Filtering has a better accuracy (0.13 μs versus 0.18 μs standard deviations), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.
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comparison of active set method deconvolution and Matched Filtering for derivation of an ultrasound transit time spectrum
Faculty of Health; Institute of Health and Biomedical Innovation; Science & Engineering Faculty, 2015Co-Authors: Marieluise Wille, Michael Zapf, Nicole V Ruiter, H Gemmeke, Christian M LangtonAbstract:The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a Matched-Filtering approach to the input and output signals. This study compares the Matched-Filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The Matched-Filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while Matched-Filtering has a better accuracy (0.13 μs vs. 0.18 μs standard deviation), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.
Marieluise Wille - One of the best experts on this subject based on the ideXlab platform.
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comparison of active set method deconvolution and Matched Filtering for derivation of an ultrasound transit time spectrum
Physics in Medicine and Biology, 2015Co-Authors: Marieluise Wille, Michael Zapf, Nicole V Ruiter, H Gemmeke, Christian M LangtonAbstract:The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a Matched-Filtering approach to the input and output signals. This study compares the Matched-Filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The Matched-Filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while Matched-Filtering has a better accuracy (0.13 μs versus 0.18 μs standard deviations), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.
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comparison of active set method deconvolution and Matched Filtering for derivation of an ultrasound transit time spectrum
Faculty of Health; Institute of Health and Biomedical Innovation; Science & Engineering Faculty, 2015Co-Authors: Marieluise Wille, Michael Zapf, Nicole V Ruiter, H Gemmeke, Christian M LangtonAbstract:The quality of ultrasound computed tomography imaging is primarily determined by the accuracy of ultrasound transit time measurement. A major problem in analysis is the overlap of signals making it difficult to detect the correct transit time. The current standard is to apply a Matched-Filtering approach to the input and output signals. This study compares the Matched-Filtering technique with active set deconvolution to derive a transit time spectrum from a coded excitation chirp signal and the measured output signal. The ultrasound wave travels in a direct and a reflected path to the receiver, resulting in an overlap in the recorded output signal. The Matched-Filtering and deconvolution techniques were applied to determine the transit times associated with the two signal paths. Both techniques were able to detect the two different transit times; while Matched-Filtering has a better accuracy (0.13 μs vs. 0.18 μs standard deviation), deconvolution has a 3.5 times improved side-lobe to main-lobe ratio. A higher side-lobe suppression is important to further improve image fidelity. These results suggest that a future combination of both techniques would provide improved signal detection and hence improved image fidelity.
Katherine A Rosenfeld - One of the best experts on this subject based on the ideXlab platform.
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detecting weak spectral lines in interferometric data through Matched Filtering
The Astronomical Journal, 2018Co-Authors: Ryan A Loomis, Karin I Oberg, Sean M Andrews, Catherine Walsh, Ian Czekala, Jane Huang, Katherine A RosenfeldAbstract:Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (S/N) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves S/N while simultaneously avoiding the computational cost and ambiguities associated with imaging, making Matched Filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate Matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH3OH in the protoplanetary disk around TW Hya, the technique yields a ≈53% S/N boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at http://github.com/AstroChem/VISIBLE.
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detecting weak spectral lines in interferometric data through Matched Filtering
arXiv: Instrumentation and Methods for Astrophysics, 2018Co-Authors: Ryan A Loomis, Karin I Oberg, Sean M Andrews, Catherine Walsh, Ian Czekala, Jane Huang, Katherine A RosenfeldAbstract:Modern radio interferometers enable observations of spectral lines with unprecedented spatial resolution and sensitivity. In spite of these technical advances, many lines of interest are still at best weakly detected and therefore necessitate detection and analysis techniques specialized for the low signal-to-noise ratio (SNR) regime. Matched filters can leverage knowledge of the source structure and kinematics to increase sensitivity of spectral line observations. Application of the filter in the native Fourier domain improves SNR while simultaneously avoiding the computational cost and ambiguities associated with imaging, making Matched Filtering a fast and robust method for weak spectral line detection. We demonstrate how an approximate Matched filter can be constructed from a previously observed line or from a model of the source, and we show how this filter can be used to robustly infer a detection significance for weak spectral lines. When applied to ALMA Cycle 2 observations of CH3OH in the protoplanetary disk around TW Hya, the technique yields a ~53% SNR boost over aperture-based spectral extraction methods, and we show that an even higher boost will be achieved for observations at higher spatial resolution. A Python-based open-source implementation of this technique is available under the MIT license at this https URL
F A Kruse - One of the best experts on this subject based on the ideXlab platform.
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analysis of imaging spectrometer data using n dimensional geometry and a mixture tuned Matched Filtering approach
IEEE Transactions on Geoscience and Remote Sensing, 2011Co-Authors: J W Boardman, F A KruseAbstract:Imaging spectrometers collect unique data sets that are simultaneously a stack of spectral images and a spectrum for each image pixel. While these data can be analyzed using approaches designed for multispectral images, or alternatively by looking at individual spectra, neither of these takes full advantage of the dimensionality of the data. Imaging spectrometer spectral radiance data or derived apparent surface reflectance data can be cast as a scattering of points in an n-dimensional Euclidean space, where n is the number of spectral channels and all axes of the n-space are mutually orthogonal. Every pixel in the data set then has a point associated with it in the n- d space, with its Cartesian coordinates defined by the values in each spectral channel. Given n-dimensional data, convex and affine geometry concepts can be used to identify the purest pixels in a given scene (the “endmembers”). N-dimensional visualization techniques permit human interpretation of all spectral information of all image pixels simultaneously and projection of the endmembers back to their locations in the imagery and to their spectral signatures. Once specific spectral endmembers are defined, partial linear unmixing (mixture-tuned Matched Filtering or “MTMF”) can be used to spectrally unmix the data and to accurately map the apparent abundance of a known target material in the presence of a composite background. MTMF incorporates the best attributes of Matched Filtering but extends that technique using the linear mixed-pixel model, thus leading to high selectivity between similar materials and minimizing classification and mapping errors for analysis of imaging spectrometer data.
Dragos Manea - One of the best experts on this subject based on the ideXlab platform.
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hyperspectral imaging based wound analysis using mixture tuned Matched Filtering classification method
Journal of Biomedical Optics, 2015Co-Authors: Mihaelaantonina Calin, Toma Coman, Sorin Viorel Parasca, Nicolae Bercaru, Roxana Savastru, Dragos ManeaAbstract:Hyperspectral imaging is a technology that is beginning to occupy an important place in medical research with good prospects in future clinical applications. We evaluated the role of hyperspectral imaging in association with a mixture-tuned Matched Filtering method in the characterization of open wounds. The methodology and the processing steps of the hyperspectral image that have been performed in order to obtain the most useful information about the wound are described in detail. Correlations between the hyperspectral image and clinical examination are described, leading to a pattern that permits relative evaluation of the square area of the wound and its different components in comparison with the surrounding normal skin. Our results showed that the described method can identify different types of tissues that are present in the wounded area and can objectively measure their respective abundance, which proves its value in wound characterization. In conclusion, the method that was described in this preliminary case presentation shows promising results, but needs further evaluation in order to become a reliable and useful tool.