Showing papers on "Parametric Image published in 2006"

Ultrasonic diagnostic imaging system and method for detecting lesions of the liver

Abstract: Significant liver growths such as HCC lesions are detected during a contrast agent ultrasound exam by their early reception of contrast and brightening following a bolus injection, as compared with surrounding normal tissue and benign growths. A pixel classifier (30) looks for and identifies pixel or voxel regions where this early wash-in of contrast occurs and denotes these pixel or voxel locations in a parametric image. The pixel classifier analyzes pixel or voxel values from a sequence of images and identifies suspicious regions in an image by uniquely coding the points in a parametric liver image where early wash-in occurs .

Continuous-wave phase-sensitive parametric image amplification

Abstract: We study experimentally parametric amplification in the continuous regime using a transverse-degenerate type-II Optical Parametric Oscillator operated below threshold. We demonstrate that this device is able to amplify either in the phase insensitive or phase sensitive way first a single mode beam, then a multimode image. Furthermore the total intensities of the amplified image projected on the signal and idler polarizations are shown to be correlated at the quantum level.

Two Methods for Analysis of Dynamic Scintigraphic Images of the Heart.

Abstract: Dynamic scintigraphic images allow functional exploration of the considered organ and thus a good comprehension of the pathological phenomenon There are two different approaches to analyse such images series; by following the activity of a region of interest or by computing parametric images Both approaches may require the detection of an area of interest This paper presents two methods of analysis of synchronised dynamic scintigraphic images of the heart based on an automatic detection of the left ventricle The first method follows the time activity of the left ventricle and computes the ventricular ejection fraction The second method computes a parametric image called “covariance image” where each pixel represents a covariance coefficient that indicates if the corresponding point in the heart and the reference area evolve or not in the same direction The results of the application of the two methods on several dynamic images are presented and discussed

Parametric image amplification at low-frequency pumping

Abstract: We develop for the first time the quantum theory of parametric image amplification at low-frequency pumping. Such a process can be implemented by coupled consecutive three-frequency optical interactions. As a result, besides the amplified optical image on the signal frequency, there are two other images with frequencies which are below the pumping frequency. The theory developed takes into account the diffraction effect and group mismatch of interacting waves. For the interacting waves the expressions for mean photon numbers and signal-to-noise ratios are deduced and the dependence of these parameters on the interaction length and coordinate at the image plane are studied.

Method for tracking of contrast enhancement pattern for pharmacokinetic and parametric analysis in fast-enhancing tissues using high-resolution mri

Abstract: A method for performing a high-resolution pharmacokinetic analysis for calculation of tissue parameters for a fast-enhancing tissue enables medical personal to accuratley determine pharmacokinetic parameters in fast-enhancing tissues (1200). The method includes obtaining mask image data of the tissue when it is in a steady state condition, obtaining a time series of image data of the tissue when the contrast agent is flowing in the tissue, and increasing a spatial resolution (1220) of the time series of image data using the mask image data to obtain a time series of increased spatial resolution image data. The method further includes performing a pharmacokinetic (1230) analysis to obtain data including at least one parameter that characterizes the tissue, providing multiparameter look-up table derived from a combination of two or more parameters, and providing a display including one parameter or a parameteric image, where the parametric image is derived from the look-up table.

GA–SSD–ARC–NLM for parametric image registration

Abstract: We present the GA–SSD–ARC–NLM, a new robust parametric image registration technique based on the non–parametric image registration SSD–ARC algorithm. This new algorithm minimizes a new cost function quite different to the original non-parametric SSD-ARC, which explicitly models outlier punishments, using a combination of a genetic algorithm and the Newton–Levenberg–Marquardt method. The performance of the new method was compared against two robust registration techniques: the Lorentzian Estimator and the RANSAC method. Experimental tests using gray level images with outliers (noise) were done using the three algorithms. The goal was to find an affine transformation to match two images; the new method improves the other methods when noisy images are used.

Mixture principal component analysis for distribution volume parametric imaging in brain PET studies

Abstract: In this paper, we present a mixture principal component analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain studies. The parameters of the probabilistic mixture model are determined using an EM algorithm. The problem of interest here requires neither the accurate arterial blood measurements as the input function nor the existence of a reference region. The effects of mPCA are examined in two different ways on the basis of whether the compartmental model for tracer dynamics is considered. First, the mPCA approach itself is used to classify all voxels into the specific binding and non-specific binding groups, and the resulting power is used for revealing the underlying distribution volume (DV) image. Second, the proposed mPCA-based classification approach is incorporated as the clustering preprocessing into our earlier work to simultaneously estimate the DV parametric image and the input function. The efficiency and superiority of the proposed scheme is demonstrated by real brain PET data.

3D reconstruction of localized objects from radiographs

Abstract: We study the reconstruction of a 3D image from a limited set of radiographs. In nondestructive testing of materials, such 3D images represent the map of attenuation of the inspected material. The reconstruction task is very useful to detect the presence of localized objects inside the material (e.g., air anomalies inside a metal), and to localize their position. We introduce parametric image models, based on a voxel decomposition of the volume, and we perform voxel estimation in the maximum a posteriori sense. In particular, we develop a sparse parametrization of the localized object areas, relying on the detection and exclusion of background (i.e., metal) voxels. Then, we perform sparse image reconstruction by designing a multiresolution scheme. This scheme successively reconstructs coarse-to-fine resolution images, and affords automatic detection of localized object voxels at each resolution level. The global image reconstruction task is then performed within a very low computation time. We finally show the performance of our method on limited angle synthetic data, in terms of quality of reconstruction and of reconstruction time.

Parametric imaging of gene expression in biopsy samples utilizing the reverse transcriptase-polymerase chain reaction method

Abstract: A method is described for generating and showing the profile of one or more genes across a tissue sample. Tissue removed from the body is sliced in to thin sheets and those sheets are then divided into small portions each portion being identified as to a location in the sheet. The image of the thin slice and the position of each small portion thereof is recorded in a computer in a manner that can generate an image of the slice. Each small portion is subject to RT-PCR to identify the presence and quantity of one or more genes therein. The portion-specific data is then entered into the computer and an image of the slice is generated showing the gene specific characteristics of each small portion. The result is a parametric image of the entire slice which allows the visualization of the gene expression within each portion which can then be compared with other images of the same or adjacent tissue.

Omni-directional Image Generation Algorithm with Parametric Image Compensation

Abstract: This paper proposes an omni-directional image generation algorithm with parametric image compensation. The algorithm generates an omni-directional image by transforming each planar image to the spherical image on spherical coordinate. Parametric image compensation method is presented in order to compensate vignetting and illumination distortions caused by properties of a camera system and lighting condition. The proposed algorithm can generates realistic and seamless omni-directional video and synthesize any point of view from the stitched omni-directional image on the spherical image. Experimental results show that the proposed omni-directional system with vignetting and illumination compensation is approximately better than that which does not consider the said effects.

Iterative method for parametric image estimation in MIR

Abstract: A new X-ray imaging method called multiple-image radiography (MIR), can simultaneously produce parametric images of absorption, refraction, and ultra-small-angle scatter, while rejecting higher-angle scatter. These parametric images where shown to have a superb diagnostic abilities compared to some conventional X-ray methods (e.g. mammograms). In this paper we present a new parametric image estimation method based on physical model of image formation in MIR. Presented method uses the fact that the angular profile of the measured X-ray, after interaction with the object, can be modeled as a convolution of a Gaussian curve and intrinsic imaging system angular profile. Further, the new approach uses an iterative conjugate gradients algorithm which in combination with the model, offers improvement in parametric image estimation accuracy.