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Medical Imaging, IEEE Transactions on

Issue 8 • Date Aug. 2011

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Displaying Results 1 - 16 of 16
  • Table of contents

    Publication Year: 2011 , Page(s): C1
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  • IEEE Transactions on Medical Imaging publication information

    Publication Year: 2011 , Page(s): C2
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  • ODVBA: Optimally-Discriminative Voxel-Based Analysis

    Publication Year: 2011 , Page(s): 1441 - 1454
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2509 KB) |  | HTML iconHTML  

    Gaussian smoothing of images prior to applying voxel-based statistics is an important step in voxel-based analysis and statistical parametric mapping (VBA-SPM) and is used to account for registration errors, to Gaussianize the data and to integrate imaging signals from a region around each voxel. However, it has also become a limitation of VBA-SPM based methods, since it is often chosen empirically and lacks spatial adaptivity to the shape and spatial extent of the region of interest, such as a region of atrophy or functional activity. In this paper, we propose a new framework, named optimally-discriminative voxel-based analysis (ODVBA), for determining the optimal spatially adaptive smoothing of images, followed by applying voxel-based group analysis. In ODVBA, nonnegative discriminative projection is applied regionally to get the direction that best discriminates between two groups, e.g., patients and controls; this direction is equivalent to local filtering by an optimal kernel whose coefficients define the optimally discriminative direction. By considering all the neighborhoods that contain a given voxel, we then compose this information to produce the statistic for each voxel. Finally, permutation tests are used to obtain a statistical parametric map of group differences. ODVBA has been evaluated using simulated data in which the ground truth is known and with data from an Alzheimer's disease (AD) study. The experimental results have shown that the proposed ODVBA can precisely describe the shape and location of structural abnormality. View full abstract»

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  • Trimmed-Likelihood Estimation for Focal Lesions and Tissue Segmentation in Multisequence MRI for Multiple Sclerosis

    Publication Year: 2011 , Page(s): 1455 - 1467
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1322 KB) |  | HTML iconHTML  

    We present a new automatic method for segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. The method performs tissue classification using a model of intensities of the normal appearing brain tissues. In order to estimate the model, a trimmed likelihood estimator is initialized with a hierarchical random approach in order to be robust to MS lesions and other outliers present in real images. The algorithm is first evaluated with simulated images to assess the importance of the robust estimator in presence of outliers. The method is then validated using clinical data in which MS lesions were delineated manually by several experts. Our method obtains an average Dice similarity coefficient (DSC) of 0.65, which is close to the average DSC obtained by raters (0.66). View full abstract»

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  • A Meta Method for Image Matching

    Publication Year: 2011 , Page(s): 1468 - 1479
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1547 KB) |  | HTML iconHTML  

    This paper presents a novel system for image matching in optical endoscopy. The proposed metamatching system approaches the challenge of matching images in a complex scene by incorporating multiple matchers and a decision function. Experiments are presented for Crohn's disease lesion matching in capsule endoscopy with a metamatcher consisting of five independent matchers. We compare the performance of six different types of decision functions. Results show that the F-measure of the metamatching system containing all five matchers is 4%-7% greater than the performance of using the best matcher only, with a maximum F-measure of 0.811. The robustness of the method is validated using simulated data generated by controlled deformations of the image. We also demonstrate how the addition of simulated data to the training set can be used to augment the performance of the metamatcher by up to 10%. View full abstract»

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  • A Fast Iterated Conditional Modes Algorithm for Water–Fat Decomposition in MRI

    Publication Year: 2011 , Page(s): 1480 - 1492
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1469 KB) |  | HTML iconHTML  

    Decomposition of water and fat in magnetic resonance imaging (MRI) is important for biomedical research and clinical applications. In this paper, we propose a two-phased approach for the three-point water-fat decomposition problem. Our contribution consists of two components: 1) a background-masked Markov random field (MRF) energy model to formulate the local smoothness of field inhomogeneity; 2) a new iterated conditional modes (ICM) algorithm accounting for high-performance optimization of the MRF energy model. The MRF energy model is integrated with background masking to prevent error propagation of background estimates as well as improve efficiency. The central component of our new ICM algorithm is the stability tracking (ST) mechanism intended to dynamically track iterative stability on pixels so that computation per iteration is performed only on instable pixels. The ST mechanism significantly improves the efficiency of ICM. We also develop a median-based initialization algorithm to provide good initial guesses for ICM iterations, and an adaptive gradient-based scheme for parametric configuration of the MRF model. We evaluate the robust of our approach with high-resolution mouse datasets acquired from 7T MRI. View full abstract»

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  • Contrast-Ultrasound Diffusion Imaging for Localization of Prostate Cancer

    Publication Year: 2011 , Page(s): 1493 - 1502
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1665 KB) |  | HTML iconHTML  

    Prostate cancer is the most prevalent form of cancer in western men. An accurate early localization of prostate cancer, permitting efficient use of modern focal therapies, is currently hampered by a lack of imaging methods. Several methods have aimed at detecting microvascular changes associated with prostate cancer with limited success by quantitative imaging of blood perfusion. Differently, we propose contrast-ultrasound diffusion imaging, based on the hypothesis that the complexity of microvascular changes is better reflected by diffusion than by perfusion characteristics. Quantification of local, intravascular diffusion is performed after transrectal ultrasound imaging of an intravenously injected ultrasound contrast agent bolus. Indicator dilution curves are measured with the ultrasound scanner resolution and fitted by a modified local density random walk model, which, being a solution of the convective diffusion equation, enables the estimation of a local, diffusion-related parameter. Diffusion parametric images obtained from five datasets of four patients were compared with histology data on a pixel basis. The resulting receiver operating characteristic (curve area = 0.91) was superior to that of any perfusion-related parameter proposed in the literature. Contrast-ultrasound diffusion imaging seems therefore to be a promising method for prostate cancer localization, encouraging further research to assess the clinical reliability. View full abstract»

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  • Iterative Tensor Voting for Perceptual Grouping of Ill-Defined Curvilinear Structures

    Publication Year: 2011 , Page(s): 1503 - 1513
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2131 KB) |  | HTML iconHTML  

    In this paper, a novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures. Our approach builds upon the tensor voting and the iterative voting frameworks. Its efficacy lies on iterative refinements of curvilinear structures by gradually shifting from an exploratory to an exploitative mode. Such a mode shifting is achieved by reducing the aperture of the tensor voting fields, which is shown to improve curve grouping and inference by enhancing the concentration of the votes over promising, salient structures. The proposed technique is validated on delineating adherens junctions that are imaged through fluorescence microscopy. However, the method is also applicable for screening other organisms based on characteristics of their cell wall structures. Adherens junctions maintain tissue structural integrity and cell-cell interactions. Visually, they exhibit fibrous patterns that may be diffused, heterogeneous in fluorescence intensity, or punctate and frequently perceptual. Besides the application to real data, the proposed method is compared to prior methods on synthetic and annotated real data, showing high precision rates. View full abstract»

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  • Maximum a Posteriori Estimation of Linear Shape Variation With Application to Vertebra and Cartilage Modeling

    Publication Year: 2011 , Page(s): 1514 - 1526
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB) |  | HTML iconHTML  

    The estimation of covariance matrices is a crucial step in several statistical tasks. Especially when using few samples of a high dimensional representation of shapes, the standard maximum likelihood estimation (ML) of the covariance matrix can be far from the truth, is often rank deficient, and may lead to unreliable results. In this paper, we discuss regularization by prior knowledge using maximum a posteriori (MAP) estimates. We compare ML to MAP using a number of priors and to Tikhonov regularization. We evaluate the covariance estimates on both synthetic and real data, and we analyze the estimates' influence on a missing-data reconstruction task, where high resolution vertebra and cartilage models are reconstructed from incomplete and lower dimensional representations. Our results demonstrate that our methods outperform the traditional ML method and Tikhonov regularization. View full abstract»

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  • A New Variational Method for Erythrocyte Velocity Estimation in Wide-Field Imaging In Vivo

    Publication Year: 2011 , Page(s): 1527 - 1545
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2460 KB) |  | HTML iconHTML  

    Measuring erythrocyte velocity in individual microvessels has important applications for biomedical and functional imaging. Recent multiphoton fluorescence microscopy approaches require injecting fluorescent tracers; moreover, only one or few vessels can be imaged at a time. To overcome these shortcomings, we used CCD-based optical imaging of intrinsic absorption changes in macroscopic vascular networks to record erythrocytes' trajectories over several mm2 of cortical surface. We then demonstrate the feasibility of erythrocyte velocity estimation from such wide-field data, using two robust, independent, algorithms. The first one is a recently published Radon transform-based algorithm that estimates erythrocyte velocity locally. We adapt it to data obtained in wide-field imaging and show, for the first time, its performance on such datasets. The second (“fasttrack”) algorithm is novel. It is based on global energy minimization techniques to estimate the full spatiotemporal erythrocytes' trajectories inside vessels. We test the two algorithms on both simulated and biological data, obtained in rat cerebral cortex in a spreading depression experiment. On vessels with medium-slow erythrocyte velocities both algorithms performed well, allowing their usage as benchmark one for another. However, our novel fasttrack algorithm outperformed the other one for higher velocities, as encountered in the arterial network. View full abstract»

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  • A Four-Dimensional Registration Algorithm With Application to Joint Correction of Motion and Slice Timing in fMRI

    Publication Year: 2011 , Page(s): 1546 - 1554
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1227 KB) |  | HTML iconHTML  

    Existing groupwise image registration algorithms for longitudinal data generally ignore continuous movements and signal changes that occur throughout image acquisition. We emphasize the case of functional magnetic resonance images, which present spatio-temporal distortion due to the combination of head motion during scanning and staggered slice acquisition. While there exist techniques to correct for motion and slice timing separately, a common dilemma is to determine which correction should be applied first. This paper proposes a four-dimensional realignment algorithm to perform both tasks simultaneously. Experiments conducted on simulated datasets with known movements suggest that the proposed algorithm provides more accurate image reconstruction than the classical two-step realignment procedure (temporal then spatial) as implemented, for instance, in the statistical parametric mapping software. View full abstract»

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  • Travelling Wave Expansion: A Model Fitting Approach to the Inverse Problem of Elasticity Reconstruction

    Publication Year: 2011 , Page(s): 1555 - 1565
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1258 KB) |  | HTML iconHTML  

    In this paper, a novel approach to the problem of elasticity reconstruction is introduced. In this approach, the solution of the wave equation is expanded as a sum of waves travelling in different directions sharing a common wave number. In particular, the solutions for the scalar and vector potentials which are related to the dilatational and shear components of the displacement respectively are expanded as sums of travelling waves. This solution is then used as a model and fitted to the measured displacements. The value of the shear wave number which yields the best fit is then used to find the elasticity at each spatial point. The main advantage of this method over direct inversion methods is that, instead of taking the derivatives of noisy measurement data, the derivatives are taken on the analytical model. This improves the results of the inversion. The dilatational and shear components of the displacement can also be computed as a byproduct of the method, without taking any derivatives. Experimental results show the effectiveness of this technique in magnetic resonance elastography. Comparisons are made with other state-of-the-art techniques. View full abstract»

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  • TRIO a Technique for Reconstruction Using Intensity Order: Application to Undersampled MRI

    Publication Year: 2011 , Page(s): 1566 - 1576
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3580 KB) |  | HTML iconHTML  

    Long acquisition times are still a limitation for many applications of magnetic resonance imaging (MRI), specially in 3-D and dynamic imaging. Several undersampling reconstruction techniques have been proposed to overcome this problem. These techniques are based on acquiring less samples than specified by the Nyquist criterion and estimating the nonacquired data by using some sort of prior information. Most of these reconstruction methods use prior information based on estimations of the pixel intensities of the images and therefore they are prone to introduce spatial or temporal blurring. Instead of using the pixel intensities, we propose to use information that allows us to sort the pixels of an image from darkest to brightest. The set of order relations which sort the pixels of an image has been called intensity order. The intensity order of an image can be estimated from low-resolution images, adjacent slices in volumetric acquisitions, temporal correlation in dynamic sequences or from prior reconstructions. Our technique for reconstruction using intensity order (TRIO) consists of looking for an image that satisfies the intensity order and minimizes the discrepancy between the acquired and reconstructed data. Results show that TRIO can effectively reconstruct 2-D-cine cardiac MR images (under-sampling factor of 4), estimating correctly the temporal evolution of the objects. Furthermore, TRIO is used as a second stage reconstruction after reconstructing with other techniques, keyhole, sliding window and k-t BLAST, to estimate the order information. In all cases the images are improved by TRIO. View full abstract»

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  • Retinal Oximetry Based on Nonsimultaneous Image Acquisition Using a Conventional Fundus Camera

    Publication Year: 2011 , Page(s): 1577 - 1580
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2012 KB) |  | HTML iconHTML  

    To measure the retinal arteriole and venule oxygen saturation (SO2) using a conventional fundus camera, retinal oximetry based on nonsimultaneous image acquisition was developed and evaluated. Two retinal images were sequentially acquired using a conventional fundus camera with two bandpass filters (568 nm: isobestic, 600 nm: nonisobestic wavelength), one after another, instead of a built-in green filter. The images were registered to compensate for the differences caused by eye movements during the image acquisition. Retinal SO2 was measured using two wavelength oximetry. To evaluate sensitivity of the proposed method, SO2 in the arterioles and venules before and after inhalation of 100% O2 were compared, respectively, in 11 healthy subjects. After inhalation of 100% O2, SO2 increased from 96.0 ± 6.0% to 98.8% ± 7.1% in the arterioles (p = 0.002) and from 54.0 ± 8.0% to 66.7% ± 7.2% in the venules (p = 0.005) (paired t-test, n = 11). Reproducibility of the method was 2.6% and 5.2% in the arterioles and venules, respectively (average standard deviation of five measurements, n = 11). View full abstract»

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  • IEEE Transactions on Medical Imaging Information for authors

    Publication Year: 2011 , Page(s): C3
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    Publication Year: 2011 , Page(s): C4
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Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structures, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.

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Meet Our Editors

Editor-in-Chief
Michael Insana
Beckman Institute for Advanced Science and Technology
Department of Bioengineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
m.f.i@ieee.org