By Topic

Medical Imaging, IEEE Transactions on

Issue 6 • Date June 2004

Filter Results

Displaying Results 1 - 16 of 16
  • Table of contents

    Page(s): c1
    Save to Project icon | Request Permissions | PDF file iconPDF (34 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

    Page(s): 661 - 675
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (573 KB)  

    We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Population-based incremental interactive concept learning for image retrieval by stochastic string segmentations

    Page(s): 676 - 689
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (605 KB) |  | HTML iconHTML  

    We propose a method for concept-based medical image retrieval that is a superset of existing semantic-based image retrieval methods. We conceive of a concept as an incremental and interactive formalization of the user's conception of an object in an image. The premise is that such a concept is closely related to a user's specific preferences and subjectivity and, thus, allows to deal with the complexity and content-dependency of medical image content. We describe an object in terms of multiple continuous boundary features and represent an object concept by the stochastic characteristics of an object population. A population-based incrementally learning technique, in combination with relevance feedback, is then used for concept customization. The user determines the speed and direction of concept customization using a single parameter that defines the degree of exploration and exploitation of the search space. Images are retrieved from a database in a limited number of steps based upon the customized concept. To demonstrate our method we have performed concept-based image retrieval on a database of 292 digitized X-ray images of cervical vertebrae with a variety of abnormalities. The results show that our method produces precise and accurate results when doing a direct search. In an open-ended search our method efficiently and effectively explores the search space. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonuniform noise propagation by using the ramp filter in fan-beam computed tomography

    Page(s): 690 - 695
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (315 KB) |  | HTML iconHTML  

    It is observed that when the homogeneity property of the ramp filter is used to derive a filtered backprojection algorithm in fan-beam tomography, the reconstructed images have nonstationary frequency components and nonstationary noise. When a short focal-length is used, higher frequency components are amplified more at the edge of the image than at the center of the image, resulting in higher noise at the edge of the image. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image denoising based on multiscale singularity detection for cone beam CT breast imaging

    Page(s): 696 - 703
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB) |  | HTML iconHTML  

    It was recently reported that the real-time flat panel detector-based cone-beam computed tomography (CBCT) breast imaging can help improve the detectability of small breast tumors with an X-ray dose comparable to that of the conventional mammography. In this paper, an efficient denoising algorithm is proposed to further reduce the X-ray exposure level required by a CBCT scan to acquire acceptable image quality. The proposed wavelet-based denoising algorithm possesses three significant characteristics: 1) wavelet coefficients at each scale are classified into two categories: irregular coefficients, and edge-related and regular coefficients; 2) noise in irregular coefficients is reduced as much as possible without producing artifacts to the denoised images; and 3) for the edge-related and regular coefficients, if they are at the first decomposition level, they are further denoised, otherwise, no modifications are made to them so as to obtain good visual quality for diagnosis. By applying the proposed denoising algorithm to the filtered projection images, the X-ray exposure level necessary for the CBCT scan can he reduced by up to 60% while obtaining clinically acceptable image quality. This denoising result indicates that in the clinical application of CBCT breast imaging, the patient radiation dose can be significantly reduced. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Robust and objective decomposition and mapping of bifurcating vessels

    Page(s): 704 - 713
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (549 KB)  

    Computational modeling of human arteries has been broadly employed to investigate the relationships between geometry, hemodynamics and vascular disease. Recent developments in modeling techniques have made it possible to perform such analyses on realistic geometries acquired noninvasively and, thus, have opened up the possibility to extend the investigation to populations of subjects. However, for this to be feasible, novel methods for the comparison of the data obtained from large numbers of realistic models in the presence of anatomic variability must be developed. In this paper, we present an automatic technique for the objective comparison of distributions of geometric and hemodynamic quantities over the surface of bifurcating vessels. The method is based on centerlines and consists of robustly decomposing the surface into its constituent branches and mapping each branch onto a template parametric plane. The application of the technique to realistic data demonstrates how similar results are obtained over similar geometries, allowing for proper model-to-model comparison. Thanks to the computational and differential geometry criteria adopted, the method does not depend on user-defined parameters or user interaction, it is flexible with respect to the bifurcation geometry and it is readily extendible to more complex configurations of interconnecting vessels. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sphere-filled organ model for virtual surgery system

    Page(s): 714 - 722
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (393 KB) |  | HTML iconHTML  

    We have been developing a virtual surgery system that is capable of simulating surgical maneuvers on elastic organs. In order to perform such maneuvers, we have created a deformable organ model using a sphere-filled method instead of the finite element method. This model is suited for real-time simulation and quantitative deformation. Furthermore, we have equipped this model with a sense of touch and a sense of force by connecting it to a force feedback device. However, in the initial stage the model became problematic when faced with complicated incisions. Therefore, we modified this model by developing an algorithm for organ deformation that performs various, complicated incisions while taking into account the effect of gravity. As a result, the sphere-filled model allowed our system to respond to various incisions that deform the organ. Thus, various physical manipulations that involve pressing, pinching, or incising an organ's surface can be performed. Furthermore, the deformation of the internal organ structures and changes in organ vasculature can be observed via the internal spheres' behavior. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Bayesian morphometry algorithm

    Page(s): 723 - 737
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (679 KB) |  | HTML iconHTML  

    Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Sampling and aliasing consequences of quarter-detector offset use in helical CT

    Page(s): 738 - 749
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (500 KB) |  | HTML iconHTML  

    In this paper, the sampling and aliasing consequences of employing a quarter-detector-offset (QDO) in helical computed tomography (CT) are analyzed. QDO is often used in conventional CT to reduce in-plane aliasing by eliminating data redundancies to improve radial sampling. In helical CT, these same redundancies are exploited to improve longitudinal sampling and so it might seem ill-advised to employ QDO. The relative merit of the two geometries for helical CT is studied by conducting a multidimensional sampling analysis of projection-space sampling as well as a Fourier crosstalk analysis of crosstalk among the object's Fourier basis components. Both a standard fanbeam helical CT geometry and a hypothetical parallel-beam CT geometry, which helps illuminate the more complicated fanbeam results, are analyzed. Using the sampling analysis, it was found that the use of QDO leads to very different spectral tiling than arise when not using QDO. However, due to the shape of the essential support of the projection data spectra that arises in practice, both configurations lead to very similar or identical amounts of spectral overlap. This perspective also predicts the spatially variant longitudinal aliasing that has been observed in helical CT. The crosstalk results were consistent with those of the multidimensional sampling analysis. Thus, from the standpoint of aliasing and crosstalk, no compelling difference is found between the two geometries. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analytic determination of the resolution-equivalent effective diameter of a pinhole collimator

    Page(s): 750 - 763
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (521 KB) |  | HTML iconHTML  

    To account for photon penetration, the formulas used to calculate the geometric resolution of a pinhole collimator use an equivalent diameter de rather than the physical diameter of the aperture. The expression commonly used for de, however, was originally derived to account for penetration in sensitivity calculations. In this paper, we show that the concept of equivalent diameter is also applicable to resolution calculations, propose angular-dependent expressions for de specific to resolution calculations, and discuss the limits of their applicability and how they compare to other expressions. Results show that for normal incidence Paix's expression for de tends to overestimate the resolution-equivalent diameter for full-width-at-half-maximum resolution, whereas Anger's is a better approximation, but may produce underestimates for submillimeter resolution imagers, especially in the case of high-energy photons. For grazing incidence, both expressions may result in significant overestimates. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Differentiation and characterization of rat mammary fibroadenomas and 4T1 mouse carcinomas using quantitative ultrasound imaging

    Page(s): 764 - 771
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (434 KB) |  | HTML iconHTML  

    Scatterer properties like the average effective scatterer diameter and acoustic concentration were determined in vivo using a quantitative ultrasound (QUS) technique from two tumor phenotypes grown in animal models. These tumor models included spontaneously occurring mammary fibroadenomas in rats and transplanted 4T1 mammary carcinomas in mice. The scatterer properties of average scatterer diameter and acoustic concentration were estimated using a Gaussian form factor from the backscattered ultrasound measured from both types of tumors. QUS images of the tumors were constructed utilizing estimated scatterer properties from regions in the tumors. The QUS images showed a clear distinction between the two types of tumors and a statistically significant difference existed between their estimated scatterer properties. The average scatterer diameter and acoustic concentration for the mammary fibroadenomas were estimated to be 105±25 μm and -15.6±5 dB (mm-3), respectively. The average scatterer diameter and acoustic concentration for the carcinomas was estimated to be 28±4.6 μm and 10.6±6.9 dB (mm-3), respectively. The distinctions in the scattering properties are clearly seen in the QUS images of the tumors and indicate that QUS imaging can be useful in differentiating between different types of mammary tumors. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An interacting multiple model probabilistic data association filter for cavity boundary extraction from ultrasound images

    Page(s): 772 - 784
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1051 KB)  

    This paper presents a novel segmentation technique for extracting cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity toward its boundary. The distance of the cavity boundary from the seed point is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius include the measurement of the object position and some false returns. The modeling approach enables us to use the interacting multiple model estimator along with a probabilistic data association filter, for contour extraction. The convergence rate of the method is very fast because it does not employ any numerical optimization. The robustness and accuracy of the method are demonstrated by segmenting contours from a series of ultrasound images. The results are validated through comparison with manual segmentations performed by an expert. An application of the method in segmenting bone contours from computed tomography images is also presented. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

    Page(s): 785 - 788
    Save to Project icon | Request Permissions | PDF file iconPDF (1915 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): c3
    Save to Project icon | Request Permissions | PDF file iconPDF (26 KB)  
    Freely Available from IEEE
  • Blank page [back cover]

    Page(s): c4
    Save to Project icon | Request Permissions | PDF file iconPDF (2 KB)  
    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