By Topic

Medical Imaging, IEEE Transactions on

Issue 6 • Date June 2005

Filter Results

Displaying Results 1 - 19 of 19
  • Table of contents

    Page(s): c1 - c4
    Save to Project icon | Request Permissions | PDF file iconPDF (37 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Medical Imaging publication information

    Page(s): c2
    Save to Project icon | Request Permissions | PDF file iconPDF (45 KB)  
    Freely Available from IEEE
  • Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes

    Page(s): 697 - 711
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3498 KB) |  | HTML iconHTML  

    In this paper, we propose a combination of mean-shift-based tracking processes to establish migrating cell trajectories through in vitro phase-contrast video microscopy. After a recapitulation on how the mean-shift algorithm permits efficient object tracking we describe the proposed extension and apply it to the in vitro cell tracking problem. In this application, the cells are unmarked (i.e., no fluorescent probe is used) and are observed under classical phase-contrast microscopy. By introducing an adaptive combination of several kernels, we address several problems such as variations in size and shape of the tracked objects (e.g., those occurring in the case of cell membrane extensions), the presence of incomplete (or noncontrasted) object boundaries, partially overlapping objects and object splitting (in the case of cell divisions or mitoses). Comparing the tracking results automatically obtained to those generated manually by a human expert, we tested the stability of the different algorithm parameters and their effects on the tracking results. We also show how the method is resistant to a decrease in image resolution and accidental defocusing (which may occur during long experiments, e.g., dozens of hours). Finally, we applied our methodology on cancer cell tracking and showed that cytochalasin-D significantly inhibits cell motility. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Study of temporal stationarity and spatial consistency of fMRI noise using independent component analysis

    Page(s): 712 - 718
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (887 KB) |  | HTML iconHTML  

    Spatial independent component analysis (ICA) was used to study the temporal stationarity and spatial consistency of structured functional MRI (fMRI) noise. Spatial correlations have been used in the past to generate filters for the removal of structured noise for each time-course in an fMRI dataset. It would be beneficial to produce a multivariate filter based on the same principles. ICA is examined to determine if it has properties that are beneficial for this type of filtering. Six fMRI baseline datasets were decomposed via spatial ICA. The time-courses associated with each component were tested for wide-sense stationarity using the wide sense stationarity quotient (WSS). Each dataset was divided into three subsets and each subset was decomposed. The components of first and third subset were matched by the strength of their correlation. The components produced by ICA were found to have largely nonstationary time-courses. Despite the temporal nonstationarity in the data, ICA was found to produce consistent spatial components. The degree of correlation among components differed depending on the amount of dimension reduction performed on the data. It was found that a relatively small number of dimensions produced components that are potentially useful for generating a spatial fMRI filter. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Wavelet coding of volumetric medical images for high throughput and operability

    Page(s): 719 - 727
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (514 KB) |  | HTML iconHTML  

    This paper presents a new three-dimensional (3-D) wavelet-based scalable lossless coding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3-D dyadic wavelet transform tailored to volumetric medical images and an optimized Rice code of very low complexity. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A stochastic model for studying the laminar structure of cortex from MRI

    Page(s): 728 - 742
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (963 KB) |  | HTML iconHTML  

    The human cerebral cortex is a laminar structure about 3 mm thick, and is easily visualized with current magnetic resonance (MR) technology. The thickness of the cortex varies locally by region, and is likely to be influenced by such factors as development, disease and aging. Thus, accurate measurements of local cortical thickness are likely to be of interest to other researchers. We develop a parametric stochastic model relating the laminar structure of local regions of the cerebral cortex to MR image data. Parameters of the model include local thickness, and statistics describing white, gray and cerebrospinal fluid (CSF) image intensity values as a function of the normal distance from the center of a voxel to a local coordinate system anchored at the gray/white matter interface. Our fundamental data object, the intensity-distance histogram (IDH), is a two-dimensional (2-D) generalization of the conventional 1-D image intensity histogram, which indexes voxels not only by their intensity value, but also by their normal distance to the gray/white interface. We model the IDH empirically as a marked Poisson process with marking process a Gaussian random field model of image intensity indexed against normal distance. In this paper, we relate the parameters of the IDH model to the local geometry of the cortex. A maximum-likelihood framework estimates the parameters of the model from the data. Here, we show estimates of these parameters for 10 volumes in the posterior cingulate, and 6 volumes in the anterior and posterior banks of the central sulcus. The accuracy of the estimates is quantified via Cramer-Rao bounds. We believe that this relatively crude model can be extended in a straightforward fashion to other biologically and theoretically interesting problems such as segmentation, surface area estimation, and estimating the thickness distribution in a variety of biologically relevant contexts. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Despeckling of medical ultrasound images using data and rate adaptive lossy compression

    Page(s): 743 - 754
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2927 KB) |  | HTML iconHTML  

    A novel technique for despeckling the medical ultrasound images using lossy compression is presented. The logarithm of the input image is first transformed to the multiscale wavelet domain. It is then shown that the subband coefficients of the log-transformed ultrasound image can be successfully modeled using the generalized Laplacian distribution. Based on this modeling, a simple adaptation of the zero-zone and reconstruction levels of the uniform threshold quantizer is proposed in order to achieve simultaneous despeckling and quantization. This adaptation is based on: 1) an estimate of the corrupting speckle noise level in the image; 2) the estimated statistics of the noise-free subband coefficients; and 3) the required compression rate. The Laplacian distribution is considered as a special case of the generalized Laplacian distribution and its efficacy is demonstrated for the problem under consideration. Context-based classification is also applied to the noisy coefficients to enhance the performance of the subband coder. Simulation results using a contrast detail phantom image and several real ultrasound images are presented. To validate the performance of the proposed scheme, comparison with two two-stage schemes, wherein the speckled image is first filtered and then compressed using the state-of-the-art JPEG2000 encoder, is presented. Experimental results show that the proposed scheme works better, both in terms of the signal to noise ratio and the visual quality. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Computer vision elastography: speckle adaptive motion estimation for elastography using ultrasound sequences

    Page(s): 755 - 766
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (911 KB) |  | HTML iconHTML  

    We present the development and validation of an image based speckle tracking methodology, for determining temporal two-dimensional (2-D) axial and lateral displacement and strain fields from ultrasound video streams. We refine a multiple scale region matching approach incorporating novel solutions to known speckle tracking problems. Key contributions include automatic similarity measure selection to adapt to varying speckle density, quantifying trajectory fields, and spatiotemporal elastograms. Results are validated using tissue mimicking phantoms and in vitro data, before applying them to in vivo musculoskeletal ultrasound sequences. The method presented has the potential to improve clinical knowledge of tendon pathology from carpel tunnel syndrome, inflammation from implants, sport injuries, and many others. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A method to track cortical surface deformations using a laser range scanner

    Page(s): 767 - 781
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4950 KB) |  | HTML iconHTML  

    This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A registration framework for the comparison of mammogram sequences

    Page(s): 782 - 790
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1105 KB) |  | HTML iconHTML  

    In this paper, we present a two-stage algorithm for mammogram registration, the geometrical alignment of mammogram sequences. The rationale behind this paper stems from the intrinsic difficulties in comparing mammogram sequences. Mammogram comparison is a valuable tool in national breast screening programs as well as in frequent monitoring and hormone replacement therapy (HRT). The method presented in this paper aims to improve mammogram comparison by estimating the underlying geometric transformation for any mammogram sequence. It takes into consideration the various temporal changes that may occur between successive scans of the same woman and is designed to overcome the inconsistencies of mammogram image formation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear phase correction with an extended statistical algorithm

    Page(s): 791 - 798
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (772 KB) |  | HTML iconHTML  

    This paper presents a new magnetic resonance imaging (MRI) phase correction method. The linear phase correction method using autocorrelation proposed by Ahn and Cho (AC method) is extended to handle nonlinear terms, which are often important for polynomial expansion of phase variation in MRI. The polynomial coefficients are statistically determined from a cascade series of n-pixel-shift rotational differential fields (RDFs). The n-pixel-shift RDF represents local vector rotations of a complex field relative to itself after being shifted by n pixels. We have found that increasing the shift enhances the signal significantly and extends the AC method to handle higher order nonlinear phase error terms. The n-pixel-shift RDF can also be applied to improve other methods such as the weighted least squares phase unwrapping method proposed by Liang. The feasibility of the method has been demonstrated with two-dimensional (2-D) in vivo inversion-recovery MRI data. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Rapid gridding reconstruction with a minimal oversampling ratio

    Page(s): 799 - 808
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1942 KB) |  | HTML iconHTML  

    Reconstruction of magnetic resonance images from data not falling on a Cartesian grid is a Fourier inversion problem typically solved using convolution interpolation, also known as gridding. Gridding is simple and robust and has parameters, the grid oversampling ratio and the kernel width, that can be used to trade accuracy for computational memory and time reductions. We have found that significant reductions in computation memory and time can be obtained while maintaining high accuracy by using a minimal oversampling ratio, from 1.125 to 1.375, instead of the typically employed grid oversampling ratio of two. When using a minimal oversampling ratio, appropriate design of the convolution kernel is important for maintaining high accuracy. We derive a simple equation for choosing the optimal Kaiser-Bessel convolution kernel for a given oversampling ratio and kernel width. As well, we evaluate the effect of presampling the kernel, a common technique used to reduce the computation time, and find that using linear interpolation between samples adds negligible error with far less samples than is necessary with nearest-neighbor interpolation. We also develop a new method for choosing the optimal presampled kernel. Using a minimal oversampling ratio and presampled kernel, we are able to perform a three-dimensional (3-D) reconstruction in one-eighth the time and requiring one-third the computer memory versus using an oversampling ratio of two and a Kaiser-Bessel convolution kernel, while maintaining the same level of accuracy. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • New denoising scheme for magnetic resonance spectroscopy signals

    Page(s): 809 - 816
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (431 KB) |  | HTML iconHTML  

    A new scheme for denoising magnetic resonance spectroscopy (MRS) signals is presented. This scheme is based on projecting noisy MRS signals in different domains, consecutively, and performing noise filtering operations in these domains. The domains are chosen such that the noise portion, which is inseparable from the desired signal in one domain, is separable in the other. A set of stable, linear, time-frequency (SLTF) transforms with different resolutions was selected for these projections as an example. Scheme evaluation was performed using extensive MRS signals with various noise levels. Compared with one domain denoising, it was observed that the proposed scheme gives superior results that compensate for the excess computational requirements. The proposed scheme supersedes also the wavelet packet denoising schemes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Weighted expectation maximization reconstruction algorithms for thermoacoustic tomography

    Page(s): 817 - 820
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (662 KB) |  | HTML iconHTML  

    Thermoacoustic tomography (TAT) is an emerging imaging technique with potential for a wide range of biomedical imaging applications. In this correspondence, we propose an infinite family of weighted expectation maximization (EM) algorithms for reconstruction of images from temporally truncated TAT measurement data. The weighted EM algorithms are equivalent mathematically to the conventional EM algorithm, but are shown to propagate data inconsistencies in different ways. Using simulated and experimental TAT measurement data, we demonstrate that suitable choices of weighted EM algorithms can effectively mitigate image artifacts that are attributable to temporal truncation of the TAT data function. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

    Page(s): 821 - 825
    Save to Project icon | Request Permissions | PDF file iconPDF (4323 KB)  
    Freely Available from IEEE
  • Special issue on volumetric reconstruction of medical images

    Page(s): 826
    Save to Project icon | Request Permissions | PDF file iconPDF (123 KB)  
    Freely Available from IEEE
  • 2006 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'06)

    Page(s): 827
    Save to Project icon | Request Permissions | PDF file iconPDF (568 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Neural Systems & Rehabilitation Engineering search for editor-in-chief

    Page(s): 828
    Save to Project icon | Request Permissions | PDF file iconPDF (93 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 (27 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