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Medical Imaging, IEEE Transactions on

Issue 8 • Date Aug. 2005

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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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  • Delineating fluid-filled region boundaries in optical coherence tomography images of the retina

    Page(s): 929 - 945
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6198 KB)  

    We evaluate the ability of a deformable model to yield accurate shape descriptions of fluid-filled regions associated with age-related macular degeneration. Calculation of retinal thickness and volume by the current optical coherence tomography (OCT) system includes fluid-filled regions or lesions along with actual retinal tissue. In order to quantify these lesions independently from the retinal tissue, they must be outlined. A deformable model was applied to OCT images of retinas demonstrating cystoids and subretinal fluid spaces. Several implementation issues were addressed in order to choose appropriate parameters. The use of a nonlinear anisotropic diffusion filter to suppress speckle noise while at the same time preserving the edges of the original image was explored. Once the contours of the lesions were outlined, quantitative analysis of the surface area and volume of the lesions was performed. The deformable model could accurately outline fluid-filled regions within the retina. The detection method tested proved effective in capturing the complexity of fluid-filled regions in OCT images. Deformable models combined with nonlinear anisotropic diffusion filtering show promise in the detection of retinal features of interest for diagnosis in clinical OCT images. Thus, fluid-filled region detection may significantly aid in analysis of treatments and diagnosis. View full abstract»

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  • The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine

    Page(s): 946 - 956
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2493 KB) |  | HTML iconHTML  

    Automated tongue image segmentation, in Chinese medicine, is difficult due to two special factors: 1) there are many pathological details on the surface of the tongue, which have a large influence on edge extraction; 2) the shapes of the tongue bodies captured from various persons (with different diseases) are quite different, so they are impossible to describe properly using a predefined deformable template. To address these problems, in this paper, we propose an original technique that is based on a combination of a bi-elliptical deformable template (BEDT) and an active contour model, namely the bi-elliptical deformable contour (BEDC). The BEDT captures gross shape features by using the steepest decent method on its energy function in the parameter space. The BEDC is derived from the BEDT by substituting template forces for classical internal forces, and can deform to fit local details. Our algorithm features fully automatic interpretation of tongue images and a consistent combination of global and local controls via the template force. We apply the BEDC to a large set of clinical tongue images and present experimental results. View full abstract»

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  • A new path planning algorithm for maximizing visibility in computed tomography colonography

    Page(s): 957 - 968
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1948 KB) |  | HTML iconHTML  

    In virtual colonoscopy, minimizing the blind areas is important for accurate diagnosis of colonic polyps. Although useful for describing the shape of an object, the centerline is not always the optimal camera path for observing the object. Hence, conventional methods in which the centerline is directly used as a path produce considerable blind areas, especially in areas of high curvature. Our proposed algorithm first approximates the surface of the object by estimating the overall shape and cross-sectional thicknesses. View positions and their corresponding view directions are then jointly determined to enable us to maximally observe the approximated surface. Moreover, by adopting bidirectional navigations, we may reduce the blind area blocked by haustral folds. For comfortable navigation, we carefully smoothen the obtained path and minimize the amount of rotation between consecutive rendered images. For the evaluation, we quantified the overall observable area on the basis of the temporal visibility that reflects the minimum interpretation time of a human observer. The experimental results show that our algorithm improves visibility coverage and also significantly reduces the number of blind areas that have a clinically meaningful size. A sequence of rendered images shows that our algorithm can provide a sequence of centered and comfortable views of colonography. View full abstract»

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  • Partial volume effect compensation for quantitative brain SPECT imaging

    Page(s): 969 - 976
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (394 KB) |  | HTML iconHTML  

    Partial volume (PV) effects degrade the quantitative accuracy of SPECT brain images. In this paper, we extended a PV compensation (PVC) method originally developed for brain PET, the geometric transfer matrix (GTM) method, to brain SPECT using iterative reconstruction-based compensations. In the GTM method a linear transform between the true regional activities and the measured results was assumed. Elements of the GTM were calculated by projecting and reconstructing maps with uniform regions representing different structures. However, with iterative reconstruction methods, especially when reconstruction-based compensation for detector response was applied, we found that it was important to treat the region maps as a perturbation to the reconstructed image in the estimation of the GTM. This modified method, termed perturbation-based GTM (pGTM) was evaluated using Monte Carlo (MC) simulated and experimentally acquired data. Results showed great improvement of the quantitative accuracy in brain SPECT imaging. For MC simulated data, PVC using pGTM reduced the underestimation of striatal activities from 30% to less than 1.2%. For experimental data, PVC using pGTM reduced the underestimation of striatal activities from 36% to less than 7.8%. The underestimation of the striatum to background activity ratio was also improved from 31% to 2.7%. View full abstract»

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  • EnPiT: filtered back-projection algorithm for helical CT using an n-Pi acquisition

    Page(s): 977 - 986
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1419 KB) |  | HTML iconHTML  

    In this paper, we formulate a reconstruction algorithm for an n-Pi acquisition, where n can be any positive odd integer. The algorithm is a generalization of the method presented in (Bontus et al. 2003). It is based on the results obtained by Katsevich (2004). For the algorithm, different sets of filter-lines have to be defined. We describe the variation of these lines along the detector in some detail, before we discuss, how the method gives all Radon-plane contributions the correct weighting. The different sets of filter-lines are all contained within the n-Pi window, such that a practical realization is possible. Reconstruction results, which we present in the final section, show convincing image quality. View full abstract»

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  • Neural networks approach to clustering of activity in fMRI data

    Page(s): 987 - 996
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (568 KB) |  | HTML iconHTML  

    Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity. View full abstract»

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  • An estimation/correction algorithm for detecting bone edges in CT images

    Page(s): 997 - 1010
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2335 KB) |  | HTML iconHTML  

    The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, the reliability is used to further correct the normal direction. The optimal scale at each point is obtained while estimating the normal direction; this scale is then used in a simple edge detector. Our experimental results have shown that use of this estimated/corrected normal direction improves the segmentation quality by decreasing the number of unexpected edges and discontinuities (gaps) of real contours. The corrected normal direction could also be used in postprocessing to delete false edges. Our segmentation algorithm is automatic, and its performance is evaluated on CT images of the human pelvis, leg, and wrist. View full abstract»

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  • An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy

    Page(s): 1011 - 1024
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1684 KB) |  | HTML iconHTML  

    The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of b- - lood flow characteristics from in vivo videos. View full abstract»

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  • Toward automated segmentation of the pathological lung in CT

    Page(s): 1025 - 1038
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2195 KB) |  | HTML iconHTML  

    Conventional methods of lung segmentation rely on a large gray value contrast between lung fields and surrounding tissues. These methods fail on scans with lungs that contain dense pathologies, and such scans occur frequently in clinical practice. We propose a segmentation-by-registration scheme in which a scan with normal lungs is elastically registered to a scan containing pathology. When the resulting transformation is applied to a mask of the normal lungs, a segmentation is found for the pathological lungs. As a mask of the normal lungs, a probabilistic segmentation built up out of the segmentations of 15 registered normal scans is used. To refine the segmentation, voxel classification is applied to a certain volume around the borders of the transformed probabilistic mask. Performance of this scheme is compared to that of three other algorithms: a conventional, a user-interactive and a voxel classification method. The algorithms are tested on 10 three-dimensional thin-slice computed tomography volumes containing high-density pathology. The resulting segmentations are evaluated by comparing them to manual segmentations in terms of volumetric overlap and border positioning measures. The conventional and user-interactive methods that start off with thresholding techniques fail to segment the pathologies and are outperformed by both voxel classification and the refined segmentation-by-registration. The refined registration scheme enjoys the additional benefit that it does not require pathological (hand-segmented) training data. View full abstract»

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  • Stereopsis-guided brain shift compensation

    Page(s): 1039 - 1052
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4121 KB) |  | HTML iconHTML  

    Brain deformation models have proven to be a powerful tool in compensating for soft tissue deformation during image-guided neurosurgery. The accuracy of these models can be improved by incorporating intraoperative measurements of brain motion. We have designed and implemented a passive intraoperative stereo vision system capable of estimating the three-dimensional shape of the surgical scene in near real-time. This intraoperative shape is compared with the cortical surface in the co-registered preoperative magnetic resonance (MR) volume for the estimation of the cortical motion resulting from the open cranial surgery. The estimated cortical motion is then used to guide a full brain model, which updates a preoperative MR volume. We have found that the stereo vision system is accurate to within approximately 1 mm. Based on data from two representative clinical cases, we show that stereopsis guidance improves the accuracy of brain shift compensation both at and below the cortical surface. View full abstract»

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  • A method for determination of the timing stability of PET scanners

    Page(s): 1053 - 1057
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (462 KB) |  | HTML iconHTML  

    We report on the timing resolution and stability of the MicroPET R4 PET scanner. Its detectors have energy resolutions in the range of 25% and previously reported timing resolutions 3.2 ns. Our preliminary evaluation of this instrument showed that artefact-free normalization sinograms could only be obtained with a timing window of 10 ns or more in spite of a timing resolution of 3.2 ns. This instrument uses high-speed electronics albeit with 2-ns timing clock. We performed sham transmission scans with nothing in the field of view, and a range of timing windows from 2 to 14 ns and used a 14-ns timing blank scan to generate effective attenuation sinograms as a function of timing window. These showed trues count-rates which fit well to a ERF (τ) function. However, the effective attenuation value, which should be 1.0, changes from block to block and becomes very high in some blocks (>3.5 at 6 ns) suggesting the need for timing alignment. A method was devised to measure the timing stability to a precision better than the timing bin width of 2 ns. View full abstract»

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  • A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

    Page(s): 1058 - 1066
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    Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm. View full abstract»

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  • Correction of bias field in MR images using singularity function analysis

    Page(s): 1067 - 1085
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2672 KB) |  | HTML iconHTML  

    A new approach for correcting bias field in magnetic resonance (MR) images is proposed using the mathematical model of singularity function analysis (SFA), which represents a discrete signal or its spectrum as a weighted sum of singularity functions. Through this model, an MR image's low spatial frequency components corrupted by a smoothly varying bias field are first removed, and then reconstructed from its higher spatial frequency components not polluted by bias field. The thus reconstructed image is then used to estimate bias field for final image correction. The approach does not rely on the assumption that anatomical information in MR images occurs at higher spatial frequencies than bias field. The performance of this approach is evaluated using both simulated and real clinical MR images. View full abstract»

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  • 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering Revolution in BioMedicine

    Page(s): 1086 - 1087
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  • Special issue on volumetric reconstruction of medical images

    Page(s): 1088
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  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): c3
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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