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Medical Imaging, IEEE Transactions on

Issue 11 • Date Nov. 2011

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Displaying Results 1 - 18 of 18
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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  • A Reproducing Kernel Hilbert Space Approach for Q-Ball Imaging

    Page(s): 1877 - 1886
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (839 KB) |  | HTML iconHTML  

    Diffusion magnetic resonance (MR) imaging has enabled us to reveal the white matter geometry in the living human brain. The Q-ball technique is widely used nowadays to recover the orientational heterogeneity of the intra-voxel fiber architecture. This article proposes to employ the Funk-Radon transform in a Hilbert space with a reproducing kernel derived from the spherical Laplace-Beltrami operator, thus generalizing previous approaches that assume a bandlimited diffusion signal. The function estimation problem is solved within a Tikhonov regularization framework, while a Gaussian process model allows for the selection of the smoothing parameter and the specification of confidence bands. Shortcomings of Q-ball imaging are discussed. View full abstract»

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  • Modelling Prostate Motion for Data Fusion During Image-Guided Interventions

    Page(s): 1887 - 1900
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2298 KB) |  | HTML iconHTML  

    There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation. View full abstract»

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  • Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

    Page(s): 1901 - 1920
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1839 KB) |  | HTML iconHTML  

    EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed. View full abstract»

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  • Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking

    Page(s): 1921 - 1932
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2054 KB) |  | HTML iconHTML  

    Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex nonrigid deformation. In this paper, we present a robust, fast, and accurate 3D tracking algorithm: prediction based collaborative trackers (PCT). A novel one-step forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 s to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature. View full abstract»

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  • Motion-Induced Phase Error Estimation and Correction in 3D Diffusion Tensor Imaging

    Page(s): 1933 - 1940
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (842 KB) |  | HTML iconHTML  

    A multishot data acquisition strategy is one way to mitigate B0 distortion and T2* blurring for high-resolution diffusion-weighted magnetic resonance imaging experiments. However, different object motions that take place during different shots cause phase inconsistencies in the data, leading to significant image artifacts. This work proposes a maximum likelihood estimation and k-space correction of motion-induced phase errors in 3D multishot diffusion tensor imaging. The proposed error estimation is robust, unbiased, and approaches the Cramer-Rao lower bound. For rigid body motion, the proposed correction effectively removes motion-induced phase errors regardless of the k-space trajectory used and gives comparable performance to the more computationally expensive 3D iterative nonlinear phase error correction method. The method has been extended to handle multichannel data collected using phased-array coils. Simulation and in vivo data are shown to demonstrate the performance of the method. View full abstract»

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  • Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs

    Page(s): 1941 - 1950
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (629 KB) |  | HTML iconHTML  

    A decreased ratio of the width of retinal arteries to veins [arteriolar-to-venular diameter ratio (AVR)], is well established as predictive of cerebral atrophy, stroke and other cardiovascular events in adults. Tortuous and dilated arteries and veins, as well as decreased AVR are also markers for plus disease in retinopathy of prematurity. This work presents an automated method to estimate the AVR in retinal color images by detecting the location of the optic disc, determining an appropriate region of interest (ROI), classifying vessels as arteries or veins, estimating vessel widths, and calculating the AVR. After vessel segmentation and vessel width determination, the optic disc is located and the system eliminates all vessels outside the AVR measurement ROI. A skeletonization operation is applied to the remaining vessels after which vessel crossings and bifurcation points are removed, leaving a set of vessel segments consisting of only vessel centerline pixels. Features are extracted from each centerline pixel in order to assign these a soft label indicating the likelihood that the pixel is part of a vein. As all centerline pixels in a connected vessel segment should be the same type, the median soft label is assigned to each centerline pixel in the segment. Next, artery vein pairs are matched using an iterative algorithm, and the widths of the vessels are used to calculate the AVR. We trained and tested the algorithm on a set of 65 high resolution digital color fundus photographs using a reference standard that indicates for each major vessel in the image whether it is an artery or vein. We compared the AVR values produced by our system with those determined by a semi-automated reference system. We obtained a mean unsigned error of 0.06 (SD 0.04) in 40 images with a mean AVR of 0.67. A second observer using the semi-automated system obtained the same mean unsigned error of 0.06 (SD 0.05) on the set of images with a mean AVR of 0.66. The testing data and referenc- standard used in this study has been made publicly available. View full abstract»

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  • The Singular Value Filter: A General Filter Design Strategy for PCA-Based Signal Separation in Medical Ultrasound Imaging

    Page(s): 1951 - 1964
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1973 KB)  

    A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB (P <; 0.05) with over 40% reduction (P <; 0.05) in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients. View full abstract»

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  • Common-Mode Differential-Mode (CMDM) Method for Double-Nuclear MR Signal Excitation and Reception at Ultrahigh Fields

    Page(s): 1965 - 1973
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    Double-tuned radio-frequency (RF) coils for heteronuclear mangentic resonance (MR) require sufficient electromagnetic isolation between the two resonators operating at two Larmor frequencies and independent tuning in order to attain highly efficient signal acquisition at each frequency. In this work, a novel method for double-tuned coil design at 7T based on the concept of common-mode differential-mode (CMDM) was developed and tested. Common mode (CM) and differential mode (DM) currents exist within two coupled parallel transmission lines, e.g., microstrip lines, yielding two different current distributions. The electromagnetic (EM) fields of the CM and DM are orthogonal to each other, and thus, the two modes are intrinsically EM decoupled. The modes can be tuned independently to desired frequencies, thus satisfying the requirement of dual-frequency MR applications. To demonstrate the feasibility and efficiency of the proposed CMDM technique, CMDM surface coils and volume coils using microstrip transmission line for 1H and 13C MRI/MRSI were designed, constructed, and tested at 7T. Bench test results showed that the isolations between the two frequency channels of the CMDM surface coil and volume coil were better than -30 and -25 dB, respectively. High quality MR phantom images were also obtained using the CMDM coils. The performance of the CMDM technique was validated through a comparison with the conventional two-pole design method at 7T. The proposed CMDM technique can be also implemented by using other coil techniques such as lumped element method, and can be applied to designing double-tuned parallel imaging coil arrays. Furthermore, if the two resonant modes of a CMDM coil were tuned to the same frequency, the CMDM coil becomes a quadrature coil due to the intrinsic orthogonal field distribution of CM and DM. View full abstract»

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  • Robust Shape Regression for Supervised Vessel Segmentation and its Application to Coronary Segmentation in CTA

    Page(s): 1974 - 1986
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2048 KB) |  | HTML iconHTML  

    This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours. View full abstract»

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  • MR-Guided Thermotherapy of Abdominal Organs Using a Robust PCA-Based Motion Descriptor

    Page(s): 1987 - 1995
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (702 KB) |  | HTML iconHTML  

    Thermotherapies can now be guided in real-time using magnetic resonance imaging (MRI). This technique is rapidly gaining importance in interventional therapies for abdominal organs such as liver and kidney. An accurate online estimation and characterization of organ displacement is mandatory to prevent misregistration and correct for motion related thermometry artifacts. In addition, when the ablation is performed with an extracorporal heating device such as high intensity focused ultrasound (HIFU), the continuous estimation of the organ displacement is the basis for the dynamic adjustment of the focal point position to track the targeted pathological tissue. In this paper, we describe the use of an optimized principal component analysis (PCA)-based motion descriptor to characterize in real-time the complex organ deformation during the therapy. The PCA was used to detect, in a preparative learning step, spatio-temporal coherences in the motion of the targeted organ. During hyperthermia, incoherent motion patterns could be discarded, which enabled improvements in motion estimation robustness, the compensation of motion related errors in thermal maps, and the adjustment of the beam position. The suggested method was evaluated for a moving phantom, and tested in vivo in the kidney and the liver of 12 healthy volunteers under free breathing conditions. The ability to perform a MR-guided thermotherapy in vivo during HIFU intervention was finally demonstrated on a porcine kidney. View full abstract»

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  • Notice of Violation of IEEE Publication Principles
    Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting

    Page(s): 1996 - 2011
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1449 KB) |  | HTML iconHTML  

    Notice of Violation of IEEE Publication Principles

    "Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting"
    by Jingyan Wang, Yongping Li, Ying Zhang, Chao Wang, Honglan Xie, Guoling Chen, and Xin Gao
    in the IEEE Transactions on Medical Imaging, Vol. 30, No. 11, November 2011, pp. 1996-2011

    After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

    This paper contains substantial duplication of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

    Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

    "Histopathy Image Classification Using Bag of Features and Kernel Functions"
    by Juan C. Caicedo, Angel Cruz, and Fabio Gonzalez
    in Lecture Notes in Computer Science. Artificial Intelligence in Medicine, AIME-09, July 2009.Volume 5651/2009, pp 126-135
    Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic - rogramming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights. View full abstract»

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  • Erratum to “General Approach to First-Order Error Prediction in Rigid Point Registration” [Mar 11 679-693]

    Page(s): 2012
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (47 KB)  

    Presents the revised text for two paragraphs from the above titled paper (ibid., vol. 30, no. 3, pp. 679-693, Mar. 2011). View full abstract»

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  • 2012 IEEE international conference on image processing

    Page(s): 2013
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  • Why we joined ... [advertisement]

    Page(s): 2014
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  • 2012 IEEE membership form

    Page(s): 2015 - 2016
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  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): C3
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    Freely Available from IEEE

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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Milan Sonka
Iowa Institute for Biomedical Imaging
3016B SC, Department of Electrical and Computer Engineering
The University of Iowa
Iowa City, IA  52242  52242  USA
milan-sonka@uiowa.edu