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Medical Imaging, IEEE Transactions on

Issue 4 • Date April 2001

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Displaying Results 1 - 10 of 10
  • An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures

    Page(s): 257 - 270
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (418 KB)  

    This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warrant that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images. View full abstract»

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  • Biomechanical 3-D finite element modeling of the human breast using MRI data

    Page(s): 271 - 279
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (207 KB)  

    Breast tissue deformation modeling has recently gained considerable interest in various medical applications. A biomechanical model of the breast is presented using a finite element (FE) formulation. Emphasis is given to the modeling of breast tissue deformation which takes place in breast imaging procedures. The first step in implementing the FE modeling (FEM) procedure is mesh generation. For objects with irregular and complex geometries such as the breast, this step is one of the most difficult and tedious tasks. For FE mesh generation, two automated methods are presented which process MRI breast images to create a patient-specific mesh. The main components of the breast are adipose, fibroglandular and skin tissues. For modeling the adipose and fibroglandular tissues, we used eight noded hexahedral elements with hyperelastic properties, while for the skin, we chose four noded hyperelastic membrane elements. For model validation, an MR image of an agarose phantom was acquired and corresponding FE meshes were created. Based on assigned elasticity parameters, a numerical experiment was performed using the FE meshes, and good results were obtained. The model was also applied to a breast image registration problem of a volunteer's breast. Although qualitatively reasonable, further work is required to validate the results quantitatively. View full abstract»

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  • Fast EM-like methods for maximum "a posteriori" estimates in emission tomography

    Page(s): 280 - 288
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (155 KB)  

    The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties. Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1991) block sequential versions of the EM algorithm that take advantage of the scanner's geometry have been proposed in order to accelerate its convergence. In Hudson and Larkin, 1991, the ordered subsets EM (OS-EM) method was applied to the hit problem and a modification (OS-GP) to the maximum a posteriori (MAP) regularized approach without showing convergence. In Browne and DePierro, 1996, we presented a relaxed version of OS-EM. (RAMLA) that converges to an ML solution. In this paper, we present an extension of RAMLA for MAP reconstruction. We show that, if the sequence generated by this method converges, then it must converge to the true MAP solution. Experimental evidence of this convergence is also shown. To illustrate this behavior we apply the algorithm to positron emission tomography simulated data comparing its performance to OS-GP. View full abstract»

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  • Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation

    Page(s): 289 - 301
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (318 KB)  

    This paper presents a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted diagnosis (CAD). In this study, one type of morphological operation is derived to enhance disease patterns of suspected masses by cleaning up unrelated background clutters, and a model-based image segmentation is performed to localize the suspected mass areas using a stochastic relaxation labeling scheme. We discuss the importance of model selection when a finite generalized Gaussian mixture is employed, and use the information theoretic criteria to determine the optimal model structure and parameters. Examples are presented to show the effectiveness of the proposed methods on mass lesion enhancement and segmentation when applied to mammographical images. Experimental results demonstrate that the proposed method achieves a very satisfactory performance as a preprocessing procedure for mass detection in CAD. View full abstract»

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  • Computerized radiographic mass detection. II. Decision support by featured database visualization and modular neural networks

    Page(s): 302 - 313
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (261 KB)  

    For pt.I see ibid., vol.20, no.4, p.289-301 (2001). Based on the enhanced segmentation of suspicious mass areas, further development of computer-assisted mass detection may be decomposed into three distinctive machine learning tasks: (1) construction of the featured knowledge database; (2) mapping of the classified and/or unclassified data points in the datahase; and (3) development of an intelligent user interface. A decision support system may then be constructed as a complementary machine observer that should enhance the radiologists performance in mass detection, We adopt a mathematical feature extraction procedure to construct the featured knowledge database from all the suspicious mass sites localized by the enhanced segmentation. The optimal mapping of the data points is then obtained by learning the generalized normal mixtures and decision boundaries, where a probabilistic modular neural network (PMNN) is developed to carry out both soft and hard clustering. A visual explanation of the decision making is further invented as a decision support, based on an interactive visualization hierarchy through the probabilistic principal component projections of the knowledge database and the localized optimal displays of the retrieved raw data. A prototype system is developed and pilot tested to demonstrate the applicability of this framework to mammographic mass detection. View full abstract»

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  • Numerical aspects of spatio-temporal current density reconstruction from EEG-/MEG-data

    Page(s): 314 - 324
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (281 KB)  

    The determination of the sources of electric activity inside the brain from electric and magnetic measurements on the surface of the head is known to be an ill-posed problem. In this paper, a new algorithm which takes temporal a priori information modeled by the smooth activation model into account is described and compared with existing algorithms such as Tikhonov-Phillips. View full abstract»

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  • A new computational approach for cortical imaging

    Page(s): 325 - 332
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (242 KB)  

    Estimation of current or potential distribution on the cortex is used to obtain information about neural sources from the scalp recorded electroencephalogram. If the active sources in the brain are superficial, the estimated field distribution on the cortex also yields information about the active source configuration. In these cases, these methods can be used as source localization methods. In this study, we concentrate on finite-element-based cortex potential estimation. Usually these methods require surface interpolation of the recorded voltages at the electrodes onto the entire scalp surface. We propose a new computational approach which does not require the use of surface interpolation but does it implicitly and uses only the recorded data at the electrodes. We refer to this method as the systematic approach (SA). We compare the SA with the surface interpolation approach (IA) and show that the SA is able to produce somewhat better accuracy than the IA. However, the main asset is that the sensitivity of the cortical potential maps to the regularization parameter is significantly lower than with the IA. View full abstract»

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  • A proposed taxonomy for nailfold capillaries based on their morphology

    Page(s): 333 - 341
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (152 KB)  

    Certain diseases cause permanent changes to the shapes and densities of nailfold capillaries and, therefore, nailfold capillaroscopy is important as a tool for diagnosing and monitoring these diseases. The first aim of the project is to resolve differences in terminology that have developed over the years in previous work. We propose a taxonomy for nailfold capillaries that cover six descriptive classes: cuticulis, open, tortuous, crossed, bushy, and bizarre. The first three are parametric in that they may be distinguished by the ratio of capillary length to width and by the curvature of the capillary limbs. The last three are characterized by their topology; a crossed capillary has a closed area that is not connected to the image background. Bushy and bizarre capillaries have atypical shapes that are characterized by the convex hull of their skeleton. These descriptive classes may be modified according to anomalies in width and length. The second aim is to automate the classification of capillaries by encapsulating the taxonomy in an algorithm; our computer program rivals the most experienced clinicians in classifying capillaries consistently with an overall agreement of 85%, with the clinicians' majority view. This was particularly valuable in classifying borderline shapes objectively and consistently. View full abstract»

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  • An interior point iterative maximum-likelihood reconstruction algorithm incorporating upper and lower bounds with application to SPECT transmission imaging

    Page(s): 342 - 353
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (278 KB)  

    The algorithm we consider here is a block-iterative (or ordered subset) version of the inferior point algorithm for transmission reconstruction. Our algorithm is an interior point method because each vector of the iterative sequence {x k}, k= 0, 1, 2,..., satisfies the constraints a j j k j, j=1,..., J. Because it is a block-iterative algorithm that reconstructs the transmission attenuation map and places constraints above and below the pixel values of the reconstructed image, we call it the BITAB method. Computer simulations using the three-dimensional mathematical cardiac and torso phantom, reveal that the BITAB algorithm in conjunction with reasonably selected prior upper and lower bounds has the potential to improve the accuracy of the reconstructed attenuation coefficients from truncated fan beam transmission projections. By suitably selecting the bounds, it is possible to restrict the over estimation of coefficients outside the fully sampled region. that results from reconstructing truncated fan beam projections with iterative transmission algorithms such as the maximum-likelihood gradient type algorithm. View full abstract»

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  • Three-dimensional registration and fusion of ultrasound and MRI using major vessels as fiducial markers

    Page(s): 354 - 359
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (425 KB)  

    This paper describes fusion of three-dimensional (3-D) ultrasound (US) and magnetic resonance imaging (MRI) data sets, without the assistance of external fiducial markers or external position sensors. Fusion of these two modalities combines real-time 3-D ultrasound scans of soft tissue with the larger anatomical framework from MRI. The complementary information available from multiple imaging modalities warrants the development of robust fusion capabilities. We describe the data acquisition, specialized algorithms, and results for 3-D fused data from phantom studies and in vivo studies of the normal human vasculature and musculoskeletal systems. View full abstract»

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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.

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