Autoradiography potentially offers high molecular sensitivity and spatial resolution for tumor margin estimation. However, conventional autoradiography requires sectioning the sample which is destructive and labor-int...
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ISBN:
(数字)9781510614420
ISBN:
(纸本)9781510614420
Autoradiography potentially offers high molecular sensitivity and spatial resolution for tumor margin estimation. However, conventional autoradiography requires sectioning the sample which is destructive and labor-intensive. Here we describe a novel autoradiography technique that uses a flexible ultra-thin scintillator which conforms to the sample surface. Imaging with the flexible scintillator enables direct, high-resolution and high-sensitivity imaging of beta particle emissions from targeted radiotracers. The technique has the potential to identify positive tumor margins in fresh unsectioned samples during surgery, eliminating the processing time demands of conventional autoradiography. We demonstrate the feasibility of the flexible autoradiography approach to directly image the beta emissions from radiopharmaceuticals using lab experiments and GEANT-4 simulations to determine i) the specificity for F-18 compared to Tc-99m-labeled tracers ii) the sensitivity to detect signal from various depths within the tissue. We found that an image resolution of 1.5 mm was achievable with a scattering background and we estimate a minimum detectable activity concentration of 0.9 kBq/ml for F-18. We show that the flexible autoradiography approach has high potential as a technique for molecular imaging of tumor margins using F-18-FDG in a tumor xenograft mouse model imaged with a radiation-shielded EMCCD camera. Due to the advantage of conforming to the specimen, the flexible scintillator showed significantly better image quality in terms of tumor signal to whole-body background noise compared to rigid and optimally thick CaF2:Eu and BC400. The sensitivity of the technique means it is suitable for clinical translation.
An image fusion is a process used to increase the visual interpretation of images in various applications. It integrates the necessary features of two or more images into a single image without introducing artifacts. ...
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ISBN:
(纸本)9781509044429
An image fusion is a process used to increase the visual interpretation of images in various applications. It integrates the necessary features of two or more images into a single image without introducing artifacts. The traditional image fusion methods are generally successful at inserting spatial detail into the multispectral imagery despite the color information in the mechanism is distorted. The significant amount of research has been conducted over the past decade related to the application of wavelet transforms in image fusion. wavelets have gained a lot of importance due to its energy compaction and multiresolution properties. This paper presents the overview of image fusion technique and the results from a number of wavelet-based image fusion schemes are compared.
STIx is a x-ray imaging spectroscopy device to be mounted as part of the Solar Orbiter cluster. Its goal is to provide images and spectra of solar flaring regions. The device provides 30 measurements of the incoming p...
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ISBN:
(纸本)9781538615652
STIx is a x-ray imaging spectroscopy device to be mounted as part of the Solar Orbiter cluster. Its goal is to provide images and spectra of solar flaring regions. The device provides 30 measurements of the incoming photon flux which can be interpreted as spatial Fourier samples. In this paper we present a method for reconstructing the intensity image from the few provided measurements. The proposed algorithm is based on compressed sensing theory. In order to provide the needed sparsity, we build an isotropic wavelet transform which is very appropriate for the STIx measurements. The evaluation on two simulated solar flares shows the potential of the algorithm in reconstructing hard x-ray maps from STIx measurements.
In the case of accidental methane leakage on a gas production industrial site, it is essential that the risks associated with an explosion of escaped clouds are assessed. By combining spectral and spatial information,...
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ISBN:
(数字)9781510618343
ISBN:
(纸本)9781510618343
In the case of accidental methane leakage on a gas production industrial site, it is essential that the risks associated with an explosion of escaped clouds are assessed. By combining spectral and spatial information, hyperspectral technology is an attractive solution for the detection of such a cloud and for the quantification of its concentration. Total has started in 2014 a research program in partnership with Onera, called NAOMI (New Advanced Observation Methods Integration) to develop new tools for remote characterization of accidental methane plumes, especially over areas inaccessible to the personnel. From one of the results of this partnership, Onera is developing an algorithm, IMGSPEC, especially designed for this purpose, using hyperspectral acquisitions in the LWIR domain The principle of IMGSPEC consists of estimating the spectral transmission of the gas cloud using an image of the background. An acquisition image of the same scene without gas is not necessarily available however. The strong point of the algorithm is its ability to recover the signal of the background. The integrated concentration is subsequently estimated pixel by pixel constituting a ppm.m concentration map. Finally, the flow rate of the leak is calculated considering the mass of the cloud, combining concentration estimation and methane density, and the wind speed which is measured with a meteo -station for instance. This algorithm was tested in June during a specific test campaign on the Lacq platform, a Total R&D industrial site. Methane leaks have been performed regulating the following flow rates: 1 g/s, 10 g/s and 100g/s. Flow rate was estimated by IMGSPEC in near real-time following hyperspectral datacube acquisitions. Acquisition and processing times were both 4s, constituting a global flow rate estimation time below 10s.
This work presents a practical method for estimating the spatially-varying gain of the signal-dependent portion of the noise from a digital breast tomosynthesis (DBT) system. A number of imageprocessing algorithms re...
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ISBN:
(数字)9781510616363
ISBN:
(纸本)9781510616363
This work presents a practical method for estimating the spatially-varying gain of the signal-dependent portion of the noise from a digital breast tomosynthesis (DBT) system. A number of imageprocessing algorithms require previous knowledge of the noise properties of a DBT unit. However, this information is not easily available and thus must be estimated. The estimation of such parameters requires a large number of calibration images, as it often changes with acquisition angle, spatial position and radiographic factors. This could represent a barrier in the algorithm's deployment, mainly for clinical applications. Thus, we modeled the gain of the Poisson noise of a commercially available DBT unit as a function of the radiographic factors, acquisition angle, and pixel position. First, we measured the noise parameters of a clinical DBT unit by acquiring 36 sets of calibration images (raw projections) using uniform phantoms of different thicknesses, within a range of radiographic factors commonly used in clinical practice. With this information, we trained a multilayer perceptron artificial neural network (MLP-ANN) to predict the gain of the Poisson noise automatically as a function of the acquisition setup. Furthermore, we varied the number of calibration images in the learning step of the MLP-ANN to determine the minimum number of images necessary to obtain an accurate model. Results show that the MLP-ANN was able to yield the desired parameters with average error of less than 2%, using a learning dataset limited to only seven sets of calibration images. The accuracy of the model, along with its computational efficiency, makes this method an attractive tool for clinical image-based applications.
Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression scheme...
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ISBN:
(数字)9781510620766
ISBN:
(纸本)9781510620766
Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. Likewise, the correlation with a subjective test using a different representation of the point cloud data also showed poor correlation. These results suggest the need for more reliable objective quality metrics and further studies on adequate point cloud data representations.
In this paper, image registration of aerial image with reference satellite image is carried out by computing multi-level sub bands using dual tree complexwavelets. Features are extracted from sub bands and the corres...
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ISBN:
(纸本)9781509044429
In this paper, image registration of aerial image with reference satellite image is carried out by computing multi-level sub bands using dual tree complexwavelets. Features are extracted from sub bands and the corresponding features are mapped for similarity by SVD methods. The registered images are evaluated for their performance metrics and the improvements in registration process in terms of transformational parameters are evaluated. The registration process proposed is accurate as it identifies the features with six orientations and thus compares the features and performs orientations foe perfect registration. The algorithm is designed to be faster by eliminating false features thus making it suitable for real time applications.
In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize h...
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ISBN:
(纸本)9781509055593
In this paper, a robust image hashing framework is proposed using image normalization, discrete wavelet transform and singular value decomposition. The stressed motive of the proposed scheme is to obtain a randomize hash sequence which can be used for image authentication and database search. For this purpose, the image is first normalized followed by hash generation in the wavelet domain utilizing the properties of singular value decomposition (SVD). Experimental evaluations demonstrate that the proposed scheme is providing the better robustness and security.
Histogram equalization is the simplest method of image enhancement. Mean brightness and contrast are important parameters of an image. Artifacts are generated if the original mean brightness of an image is not preserv...
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ISBN:
(纸本)9781509055593
Histogram equalization is the simplest method of image enhancement. Mean brightness and contrast are important parameters of an image. Artifacts are generated if the original mean brightness of an image is not preserved. A high contrast provides good visual quality. Contrast can be increased by increasing entropy of the image. Entropy can be maximized by making the image histogram as flat as possible. In this paper, the trade-off between mean brightness and maximum entropy performed to achieve high contrast image with conserved mean brightness.
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