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Medical Imaging, IEEE Transactions on

Issue 8 • Date Aug. 2012

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Displaying Results 1 - 18 of 18
  • Table of contents

    Publication Year: 2012 , Page(s): C1
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  • IEEE Transactions on Medical Imaging publication information

    Publication Year: 2012 , Page(s): C2
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  • Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model

    Publication Year: 2012 , Page(s): 1509 - 1520
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2300 KB) |  | HTML iconHTML  

    This paper addresses the problem of jointly estimating the statistical distribution and segmenting lesions in multiple-tissue high-frequency skin ultrasound images. The distribution of multiple-tissue images is modeled as a spatially coherent finite mixture of heavy-tailed Rayleigh distributions. Spatial coherence inherent to biological tissues is modeled by enforcing local dependence between the mixture components. An original Bayesian algorithm combined with a Markov chain Monte Carlo method is then proposed to jointly estimate the mixture parameters and a label-vector associating each voxel to a tissue. More precisely, a hybrid Metropolis-within-Gibbs sampler is used to draw samples that are asymptotically distributed according to the posterior distribution of the Bayesian model. The Bayesian estimators of the model parameters are then computed from the generated samples. Simulation results are conducted on synthetic data to illustrate the performance of the proposed estimation strategy. The method is then successfully applied to the segmentation of in vivo skin tumors in high-frequency 2-D and 3-D ultrasound images. View full abstract»

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  • Three-Dimensional Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search-Graph-Cut

    Publication Year: 2012 , Page(s): 1521 - 1531
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2002 KB) |  | HTML iconHTML  

    An automated method is reported for segmenting 3-D fluid-associated abnormalities in the retina, so-called symptomatic exudate-associated derangements (SEAD), from 3-D OCT retinal images of subjects suffering from exudative age-related macular degeneration. In the first stage of a two-stage approach, retinal layers are segmented, candidate SEAD regions identified, and the retinal OCT image is flattened using a candidate-SEAD aware approach. In the second stage, a probability constrained combined graph search-graph cut method refines the candidate SEADs by integrating the candidate volumes into the graph cut cost function as probability constraints. The proposed method was evaluated on 15 spectral domain OCT images from 15 subjects undergoing intravitreal anti-VEGF injection treatment. Leave-one-out evaluation resulted in a true positive volume fraction (TPVF), false positive volume fraction (FPVF) and relative volume difference ratio (RVDR) of 86.5%, 1.7%, and 12.8%, respectively. The new graph cut-graph search method significantly outperformed both the traditional graph cut and traditional graph search approaches (p <; 0.01, p <; 0.04) and has the potential to improve clinical management of patients with choroidal neovascularization due to exudative age-related macular degeneration. View full abstract»

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  • A Box Spline Calculus for the Discretization of Computed Tomography Reconstruction Problems

    Publication Year: 2012 , Page(s): 1532 - 1541
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2940 KB) |  | HTML iconHTML  

    B-splines are attractive basis functions for the continuous-domain representation of biomedical images and volumes. In this paper, we prove that the extended family of box splines are closed under the Radon transform and derive explicit formulae for their transforms. Our results are general; they cover all known brands of compactly-supported box splines (tensor-product B-splines, separable or not) in any number of dimensions. The proposed box spline approach extends to non-Cartesian lattices used for discretizing the image space. In particular, we prove that the 2-D Radon transform of an -direction box spline is generally a (nonuniform) polynomial spline of degree . The proposed framework allows for a proper discretization of a variety of tomographic reconstruction problems in a box spline basis. It is of relevance for imaging modalities such as X-ray computed tomography and cryo-electron microscopy. We provide experimental results that demonstrate the practical advantages of the box spline formulation for improving the quality and efficiency of tomographic reconstruction algorithms. View full abstract»

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  • Gaussian Process Models of Dynamic PET for Functional Volume Definition in Radiation Oncology

    Publication Year: 2012 , Page(s): 1542 - 1556
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2368 KB) |  | HTML iconHTML  

    In routine oncologic positron emission tomography (PET), dynamic information is discarded by time-averaging the signal to produce static images of the “standardised uptake value” (SUV). Defining functional volumes of interest (VOIs) in terms of SUV is flawed, as values are affected by confounding factors and the chosen time window, and SUV images are not sensitive to functional heterogeneity of pathological tissues. Also, SUV iso-contours are highly affected by the choice of threshold and no threshold, or other SUV-based segmentation method, is universally accepted for a given VOI type. Gaussian Process (GP) time series models describe macro-scale dynamic behavior arising from countless interacting micro-scale processes, as is the case for PET signals from heterogeneous tissue. We use GPs to model time-activity curves (TACs) from dynamic PET and to define functional volumes for PET oncology. Probabilistic methods of tissue discrimination are presented along with novel contouring methods for functional VOI segmentation. We demonstrate the value of GP models for voxel classification and VOI contouring of diseased and metastatic tissues with functional heterogeneity in prostate PET. Classification experiments reveal superior sensitivity and specificity over SUV calculation and a TAC-based method proposed in recent literature. Contouring experiments reveal differences in shape between gold-standard and GP VOIs and correlation with kinetic models shows that the novel VOIs contain extra clinically relevant information compared to SUVs alone. We conclude that the proposed models offer a principled data analysis technique that improves on SUVs for oncologic VOI definition. Continuing research will generalize GP models for different oncology tracers and imaging protocols with the ultimate goal of clinical use including treatment planning. View full abstract»

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  • Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions

    Publication Year: 2012 , Page(s): 1557 - 1572
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3199 KB) |  | HTML iconHTML  

    A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, thereby reducing the uncertainty inherent in 2D interventional images. The proposed methodology is divided into two parts: global structural alignment and local nonrigid registration. In both cases, vessel centerlines are automatically extracted from the 2D fluoroscopic images, and serve as the basis for the alignment and registration algorithms. In the first part, an energy minimization method is used to estimate a global affine transformation that aligns the centerline with the angiograms. The performance of nine general purpose optimizers has been assessed for this problem, and detailed results are presented. In the second part, a fully nonrigid registration method is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process to compute a nonrigid registration. With a typical run time of less than 3 s, the algorithms are fast enough for interactive applications. Experiments on five different subjects are presented and show promising results. View full abstract»

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  • Statistical Shape Model-Based Femur Kinematics From Biplane Fluoroscopy

    Publication Year: 2012 , Page(s): 1573 - 1583
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1447 KB) |  | HTML iconHTML  

    Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 ± 0.41 mm.The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5° precision). View full abstract»

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  • Fast 3-D Tomographic Microwave Imaging for Breast Cancer Detection

    Publication Year: 2012 , Page(s): 1584 - 1592
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1921 KB) |  | HTML iconHTML  

    Microwave breast imaging (using electromagnetic waves of frequencies around 1 GHz) has mostly remained at the research level for the past decade, gaining little clinical acceptance. The major hurdles limiting patient use are both at the hardware level (challenges in collecting accurate and noncorrupted data) and software level (often plagued by unrealistic reconstruction times in the tens of hours). In this paper we report improvements that address both issues. First, the hardware is able to measure signals down to levels compatible with sub-centimeter image resolution while keeping an exam time under 2 min. Second, the software overcomes the enormous time burden and produces similarly accurate images in less than 20 min. The combination of the new hardware and software allows us to produce and report here the first clinical 3-D microwave tomographic images of the breast. Two clinical examples are selected out of 400+ exams conducted at the Dartmouth Hitchcock Medical Center (Lebanon, NH). The first example demonstrates the potential usefulness of our system for breast cancer screening while the second example focuses on therapy monitoring. View full abstract»

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  • Estimating A Reference Standard Segmentation With Spatially Varying Performance Parameters: Local MAP STAPLE

    Publication Year: 2012 , Page(s): 1593 - 1606
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2470 KB) |  | HTML iconHTML  

    We present a new algorithm, called local MAP STAPLE, to estimate from a set of multi-label segmentations both a reference standard segmentation and spatially varying performance parameters. It is based on a sliding window technique to estimate the segmentation and the segmentation performance parameters for each input segmentation. In order to allow for optimal fusion from the small amount of data in each local region, and to account for the possibility of labels not being observed in a local region of some (or all) input segmentations, we introduce prior probabilities for the local performance parameters through a new maximum a posteriori formulation of STAPLE. Further, we propose an expression to compute confidence intervals in the estimated local performance parameters. We carried out several experiments with local MAP STAPLE to characterize its performance and value for local segmentation evaluation. First, with simulated segmentations with known reference standard segmentation and spatially varying performance, we show that local MAP STAPLE performs better than both STAPLE and majority voting. Then we present evaluations with data sets from clinical applications. These experiments demonstrate that spatial adaptivity in segmentation performance is an important property to capture. We compared the local MAP STAPLE segmentations to STAPLE, and to previously published fusion techniques and demonstrate the superiority of local MAP STAPLE over other state-of-the-art algorithms. View full abstract»

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  • Volumetric Intraoperative Brain Deformation Compensation: Model Development and Phantom Validation

    Publication Year: 2012 , Page(s): 1607 - 1619
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1523 KB) |  | HTML iconHTML  

    During neurosurgery, nonrigid brain deformation may affect the reliability of tissue localization based on preoperative images. To provide accurate surgical guidance in these cases, preoperative images must be updated to reflect the intraoperative brain. This can be accomplished by warping these preoperative images using a biomechanical model. Due to the possible complexity of this deformation, intraoperative information is often required to guide the model solution. In this paper, a linear elastic model of the brain is developed to infer volumetric brain deformation associated with measured intraoperative cortical surface displacement. The developed model relies on known material properties of brain tissue, and does not require further knowledge about intraoperative conditions. To provide an initial estimation of volumetric model accuracy, as well as determine the model's sensitivity to the specified material parameters and surface displacements, a realistic brain phantom was developed. Phantom results indicate that the linear elastic model significantly reduced localization error due to brain shift, from >; 16 mm to under 5 mm, on average. In addition, though in vivo quantitative validation is necessary, preliminary application of this approach to images acquired during neocortical epilepsy cases confirms the feasibility of applying the developed model to in vivo data. View full abstract»

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  • Investigation of X-ray Fluorescence Computed Tomography (XFCT) and K-Edge Imaging

    Publication Year: 2012 , Page(s): 1620 - 1627
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1857 KB) |  | HTML iconHTML  

    This work provides a comprehensive Monte Carlo study of X-ray fluorescence computed tomography (XFCT) and K-edge imaging system, including the system design, the influence of various imaging components, the sensitivity and resolution under various conditions. We modified the widely used EGSnrc/DOSXYZnrc code to simulate XFCT images of two acrylic phantoms loaded with various concentrations of gold nanoparticles and Cisplatin for a number of XFCT geometries. In particular, reconstructed signal as a function of the width of the detector ring, its angular coverage and energy resolution were studied. We found that XFCT imaging sensitivity of the modeled systems consisting of a conventional X-ray tube and a full 2-cm-wide energy-resolving detector ring was 0.061% and 0.042% for gold nanoparticles and Cisplatin, respectively, for a dose of ~10cGy. Contrast-to-noise ratio (CNR) of XFCT images of the simulated acrylic phantoms was higher than that of transmission K-edge images for contrast concentrations below 0.4%. View full abstract»

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  • Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis

    Publication Year: 2012 , Page(s): 1628 - 1637
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (974 KB) |  | HTML iconHTML  

    We reconstruct the in vivo spatial distribution of linear and nonlinear elastic parameters in ten patients with benign (five) and malignant (five) tumors. The mechanical behavior of breast tissue is represented by a modified Veronda-Westmann model with one linear and one nonlinear elastic parameter. The spatial distribution of these elastic parameters is determined by solving an inverse problem within the region of interest (ROI). This inverse problem solution requires the knowledge of the displacement fields at small and large strains. The displacement fields are measured using a free-hand ultrasound strain imaging technique wherein, a linear array ultrasound transducer is positioned on the breast and radio frequency echo signals are recorded within the ROI while the tissue is slowly deformed with the transducer. Incremental displacement fields are determined from successive radio-frequency frames by employing cross-correlation techniques. The rectangular regions of interest were subjectively selected to obtain low noise displacement estimates and therefore were variables that ranged from 346 to 849.6 mm . It is observed that malignant tumors stiffen at a faster rate than benign tumors and based on this criterion nine out of ten tumors were correctly classified as being either benign or malignant. View full abstract»

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  • Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation

    Publication Year: 2012 , Page(s): 1638 - 1650
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2612 KB) |  | HTML iconHTML  

    Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the level-set representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the level set implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM (MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% ± 5%, and a mean surface error of 1.5 mm ± .8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% ± 9% and 1.6 mm ± 1.0 mm, respectively. The differences with respect to our levelset-based MFLAAM model are statistically significant (p <; .05). In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods. View full abstract»

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  • Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

    Publication Year: 2012 , Page(s): 1651 - 1660
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1167 KB) |  | HTML iconHTML  

    A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the statistical distribution of the parameter of interest in the database. The ranking of the methods relies on figures of merit derived from the regression and computed using a bootstrap process. The methodology was applied to the estimation of the left ventricular ejection fraction derived from cardiac magnetic resonance images segmented using eight approaches with different degrees of automation: three segmentations were entirely manually performed and the others were variously automated. The ranking of methods was consistent with the expected performance of the estimation methods: the most accurate estimates of the ejection fraction were obtained using manual segmentations. The robustness of the ranking was demonstrated when at least three methods were compared. These results suggest that the proposed statistical approach might be helpful to assess the performance of estimation methods on clinical data for which no gold standard is available. View full abstract»

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  • Ultrafast Doppler Imaging of Blood Flow Dynamics in the Myocardium

    Publication Year: 2012 , Page(s): 1661 - 1668
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1272 KB) |  | HTML iconHTML  

    Imaging intramyocardial vascular flows in real-time could strongly help to achieve better diagnostic of cardiovascular diseases. To date, no standard imaging modality allows describing accurately myocardial blood flow dynamics with good spatial and temporal resolution. We recently introduced a novel ultrasonic Doppler imaging technique based on compounded plane waves transmissions at ultrafast frame rate. The high sensitivity of this ultrafast Doppler technique permits to image the intramyocardial blood flow and its dynamics. A dedicated demodulation-filtering process is implemented to compensate for the large tissue velocity of the myocardium during the cardiac cycle. A signed power Doppler processing provides the discrimination between arterial and venous flows. Experiments were performed in vivo in a large animal open chest model (N = 5 sheep) using a conventional ultrasonic probe placed at the surface of the heart. Results show the capability of the technique to image intramyocardial vascular flows in normal physiological conditions with good spatial and temporal resolution (10 ms). Flow dynamics over the cardiac cycle were investigated and the imaging method demonstrated a phase opposition of flow waveforms between arterial and venous flows. Finally, ultrafast Doppler combined with tissue motion compensation was found able to reveal vascular flow disruption in ischemic regions during occlusion of the main diagonal coronary artery. View full abstract»

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  • IEEE Transactions on Medical Imaging information for authors

    Publication Year: 2012 , Page(s): C3
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  • [Blank page - Back cover]

    Publication Year: 2012 , 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