We postulate an optical configuration which takes a multispectral/hyperspectral scene and collects a multiplexed spectral sample on the Focal Plane Array (FPA). From such a measurement paradigm, the data is then proce...
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ISBN:
(纸本)9781628415889
We postulate an optical configuration which takes a multispectral/hyperspectral scene and collects a multiplexed spectral sample on the Focal Plane Array (FPA). From such a measurement paradigm, the data is then processed with compressive imaging techniques and we recover the full multispectral cube from a single frame of imagery. We use a trained dictionary prior assumption along with a greedy reconstruction algorithm for local multispectral reconstruction.
Sequential test algorithms are playing increasingly important roles for quick detecting network intrusions such as portscanners. In view of the fact that such algorithms are usually analyzed based on intuitive approxi...
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ISBN:
(纸本)9781628415728
Sequential test algorithms are playing increasingly important roles for quick detecting network intrusions such as portscanners. In view of the fact that such algorithms are usually analyzed based on intuitive approximation or asymptotic analysis, we develop an exact computational method for the performance analysis of such algorithms. Our method can be used to calculate the probability of false alarm and average detection time up to arbitrarily prespecified accuracy.
The recently published Vancouver model for lung nodule malignancy prediction holds great promise as a practically feasible tool to mitigate the clinical decision problem of how to act on a lung nodule detected at base...
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ISBN:
(纸本)9781628415049
The recently published Vancouver model for lung nodule malignancy prediction holds great promise as a practically feasible tool to mitigate the clinical decision problem of how to act on a lung nodule detected at baseline screening. It provides a formula to compute a probability of malignancy from only nine clinical and radiologic features. The feature values are provided by user interaction but in principle could also be automatically pre-filled by appropriate image processing algorithms and RIS requests. Nodule diameter is a feature with crucial influence on the predicted malignancy, and leads to uncertainty caused by inter-reader variability. The purpose of this paper is to analyze how strongly the malignancy prediction of a lung nodule found with CT screening is affected by the inter-reader variation of the nodule diameter estimation. To this aim we have estimated the magnitude of the malignancy variability by applying the Vancouver malignancy model to the LIDC-IDRI database which contains independent delineations from several readers. It can be shown that using fully automatic nodule segmentation can significantly lower the variability of the estimated malignancy, while demonstrating excellent agreement with the expert readers.
Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are ...
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ISBN:
(纸本)9781628418545
Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it's necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.
This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAI...
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ISBN:
(纸本)9781628415032
This paper presents an automatic method for segmentation of Multiple Sclerosis (MS) in Magnetic Resonance Images (MRI) of the brain. The approach is based on similarities between multi-channel patches (T1, T2 and FLAIR). An MS lesion patch database is built using training images for which the label maps are known. For each patch in the testing image, k similar patches are retrieved from the database. The matching labels for these k patches are then combined to produce an initial segmentation map for the test case. Finally a novel iterative patch-based label refinement process based on the initial segmentation map is performed to ensure spatial consistency of the detected lesions. A leave-one-out evaluation is done for each testing image in the MS lesion segmentation challenge of MICCAI 2008. Results are shown to compete with the state-of-the-art methods on the MICCAI 2008 challenge.
We present a novel optimisation algorithm for inverse lithography, based on optimization of the mask derivative, a domain inherently sparse, and for rectilinear polygons, invertible. The method is first developed assu...
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ISBN:
(纸本)9781628415285
We present a novel optimisation algorithm for inverse lithography, based on optimization of the mask derivative, a domain inherently sparse, and for rectilinear polygons, invertible. The method is first developed assuming a point light source, and then extended to general incoherent sources. What results is a fast algorithm, producing manufacturable masks (the search space is constrained to rectilinear polygons), and flexible (specific constraints such as minimal line widths can be imposed). One inherent trick is to treat polygons as continuous entities, thus making aerial image calculation extremely fast and accurate. Requirements for mask manufacturability can be integrated in the optimization without too much added complexity. We also explain how to extend the scheme for phase-changing mask optimization.
As advances in imaging technologies make more and more data available for biomedical applications, there is an increasing need to develop efficient quantitative algorithms for the analysis and processing of imaging da...
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ISBN:
(纸本)9781628417630
As advances in imaging technologies make more and more data available for biomedical applications, there is an increasing need to develop efficient quantitative algorithms for the analysis and processing of imaging data. In this paper, we introduce an innovative multiscale approach called Directional Ratio which is especially effective to distingush isotropic from anisotropic structures. This task is especially useful in the analysis of images of neurons, the main units of the nervous systems which consist of a main cell body called the soma and many elongated processes called neurites. We analyze the theoretical properties of our method on idealized models of neurons and develop a numerical implementation of this approach for analysis of fluorescent images of cultured neurons. We show that this algorithm is very effective for the detection of somas and the extraction of neurites in images of small circuits of neurons.
Centering the phase-center of an antenna onto the rotational axis used to measure its radiation pattern is an iterative and time consuming process. To facilitate this process, an algorithm has been developed to calcul...
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ISBN:
(纸本)9781628415773
Centering the phase-center of an antenna onto the rotational axis used to measure its radiation pattern is an iterative and time consuming process. To facilitate this process, an algorithm has been developed to calculate the phase-center offset from the axis of rotation of a 2D antenna pattern. The hybrid algorithm is comprised of a combination of the two-point method to calculate the offset along the antenna mainbeam, and an antisymmetry method is used to calculate offset perpendicular to the mainbeam direction. The algorithm is tested on the E-plane radiation pattern of a cylindrical horn antenna calculated using the HFSS electromagnetic simulation engine, radiating at 5GHz. The algorithm calculates the phase-center offset to within 15%. Because the algorithm analyzes the unwrapped phase of the radiation pattern, which it converts to offset distance, no ambiguity due to offsets greater than a wavelength exist. Using this algorithm, the phase-center of the antenna can be placed coincident to the axis of rotation after the first antenna pattern is measured and analyzed.
Reconstructing images from SAR returns is usually a time consuming task. In addition it is often desired to use as many returns as possible to achieve better image quality. However, the high computational resources de...
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ISBN:
(纸本)9781628415742
Reconstructing images from SAR returns is usually a time consuming task. In addition it is often desired to use as many returns as possible to achieve better image quality. However, the high computational resources demand by the conventional methods hinders the reconstruction process. In this article, we propose a simple method to reconstruct SAR image that is built upon the back-projection algorithm using multiple sub-aperture imagery to attain both greater processing efficiency and improved image quality. Instead of aggregating all the available pulses into one single image following the back-projection algorithm, our proposed method creates multiple SAR image reconstructions from a relatively small number of pulses to exploit variations in sub-aperture views of the scene. The heterogeneity among these sub-aperture views exhibits an exceptional difference between various objects and presents a reconstruction with much less noise. Our proposed method is evaluated with circular spotlight GOTCHA data sets and it demonstrates much improved computational performance and image quality compared to the conventional back-projection algorithm.
In this work we developed a novel denoising algorithm for DSA image series. This algorithm takes advantage of the low rank nature of the DSA image sequences to enable a dramatic reduction in radiation and/or contrast ...
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ISBN:
(纸本)9781628415025
In this work we developed a novel denoising algorithm for DSA image series. This algorithm takes advantage of the low rank nature of the DSA image sequences to enable a dramatic reduction in radiation and/or contrast doses in DSA imaging. Both spatial and temporal regularizers were introduced in the optimization algorithm to further reduce noise. To validate the method, in vivo animal studies were conducted with a Siemens Artis Zee biplane system using different radiation dose levels and contrast concentrations. Both conventionally processed DSA images and the DSA images generated using the novel denoising method were compared using absolute noise standard deviation and the contrast to noise ratio (CNR). With the application of the novel denoising algorithm for DSA, image quality can be maintained with a radiation dose reduction by a factor of 20 and/or a factor of 2 reduction in contrast dose. Image processing is completed on a GPU within a second for a 10s DSA data acquisition.
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