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Medical Imaging, IEEE Transactions on

Issue 9 • Date Sept. 2007

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Displaying Results 1 - 18 of 18
  • Table of contents

    Page(s): C1
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  • IEEE Transactions on Medical Imaging publication information

    Page(s): C2
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  • Guest Editorial Special Issue on Mathematical Modeling in Biomedical Image Analysis

    Page(s): 1133 - 1135
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  • Left Ventricular Deformation Recovery From Cine MRI Using an Incompressible Model

    Page(s): 1136 - 1153
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3436 KB) |  | HTML iconHTML  

    This paper presents a method for 3D deformation recovery of the left ventricular (LV) wall from anatomical cine magnetic resonance imaging (MRI). The method is based on a de- formable model that is incompressible, a desired property since the myocardium has been shown to be nearly incompressible. The LV wall needs to be segmented in an initial frame after which the method automatically determines the deformation everywhere in the LV wall throughout the cardiac cycle. Two studies were conducted to validate the method. In the first study, the deformation recovered from a 3D anatomical cine MRI of a healthy volunteer was compared against the manual segmentation of the LV wall and against the corresponding 3D tagged cine MRI. The average volume agreement between the model and the manual segmentation had a false positive rate of 3%, false negative rate of 3%, and true positive rate of 93%. The average distance between the model and manually determined intersections of perpendicular tag planes was 1.6 mm (1.1 pixel). Another set of 3D anatomical and tagged MRI scans was taken of the same volunteer four months later. The method was applied to the second set and the recovered deformation was very similar to the one obtained from the first set. In the second study, the method was applied to 3D anatomical cine MRI scans of three patients with ventricular dyssynchrony and three age-matched healthy volunteers. The LV wall deformations recovered for the three normals agreed well and the recovered strains were similar to those reported by other researchers for normal subjects. Strains and displacements of the three patients were clearly smaller than those of the three normals indicating reduced cardiac function. The deformation recovered for the three normals and the three patients was validated against manual segmentation and corresponding tag cine MRI scans and the agreement was similar to that of the first validation study. View full abstract»

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  • Registration of Multiview Real-Time 3-D Echocardiographic Sequences

    Page(s): 1154 - 1165
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2199 KB) |  | HTML iconHTML  

    Real-time 3-D echocardiography opens up the possibility of interactive, fast 3-D analysis of cardiac anatomy and function. However, at the present time its quantitative power cannot be fully exploited due to the limited quality of the images. In this paper, we present an algorithm to register apical and parasternal echocardiographic datasets that uses a new similarity measure, based on local orientation and phase differences. By using phase and orientation to guide registration, the effect of artifacts intrinsic to ultrasound images is minimized. The presented method is fully automatic except for initialization. The accuracy of the method was validated qualitatively, resulting in 85% of the cardiac segments estimated having a registration error smaller than 2 mm, and no segments with an error larger than 5 mm. Robustness with respect to landmark initialization was validated quantitatively, with average errors smaller than 0.2 mm and 0.5o for initialization landmarks rotations of up to 15o and translations of up to 10 mm. View full abstract»

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  • Shape-Based Normalization of the Corpus Callosum for DTI Connectivity Analysis

    Page(s): 1166 - 1178
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (841 KB) |  | HTML iconHTML  

    The continuous medial representation (cm-rep) is an approach that makes it possible to model, normalize, and analyze anatomical structures on the basis of medial geometry. Having recently presented a partial differential equation (PDE)-based approach for 3-D cm-rep modeling [1], here we present an equivalent 2-D approach that involves solving an ordinary differential equation. This paper derives a closed form solution of this equation and shows how Pythagorean hodograph curves can be used to express the solution as a piecewise polynomial function, allowing efficient and robust medial modeling. The utility of the approach in medical image analysis is demonstrated by applying it to the problem of shape-based normalization of the midsagittal section of the corpus callosum. Using diffusion tensor tractography, we show that shape- based normalization aligns subregions of the corpus callosum, defined by connectivity, more accurately than normalization based on volumetric registration. Furthermore, shape-based normalization helps increase the statistical power of group analysis in an experiment where features derived from diffusion tensor tractography are compared between two cohorts. These results suggest that cm-rep is an appropriate tool for normalizing the corpus callosum in white matter studies. View full abstract»

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  • Symmetric Data Attachment Terms for Large Deformation Image Registration

    Page(s): 1179 - 1189
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1142 KB) |  | HTML iconHTML  

    Nonrigid medical image registration between images that are linked by an invertible transformation is an inherently symmetric problem. The transformation that registers the image pair should ideally be the inverse of the transformation that registers the pair with the order of images interchanged. This property is referred to as symmetry in registration or inverse consistent registration. However, in practical estimation, the available registration algorithms have tended to produce inverse inconsistent transformations when the template and target images are interchanged. In this paper, we propose two novel cost functions in the large deformation diffeomorphic framework that are inverse consistent. These cost functions have symmetric data-attachment terms; in the first, the matching error is measured at all points along the flow between template and target, and in the second, matching is enforced only at the midpoint of the flow between the template and target. We have implemented these cost functions and present experimental results to validate their inverse consistent property and registration accuracy. View full abstract»

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  • A New Validation Method for X-ray Mammogram Registration Algorithms Using a Projection Model of Breast X-ray Compression

    Page(s): 1190 - 1200
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3931 KB) |  | HTML iconHTML  

    Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous non- rigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application. View full abstract»

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  • A Hierarchical Algorithm for MR Brain Image Parcellation

    Page(s): 1201 - 1212
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (884 KB) |  | HTML iconHTML  

    We introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the subtrees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p = 0.07) was observed instead of statistical significance. View full abstract»

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  • Vessels as 4-D Curves: Global Minimal 4-D Paths to Extract 3-D Tubular Surfaces and Centerlines

    Page(s): 1213 - 1223
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1015 KB) |  | HTML iconHTML  

    In this paper, we propose an innovative approach to the segmentation of tubular structures. This approach combines all of the benefits of minimal path techniques such as global minimizers, fast computation, and powerful incorporation of user input, while also having the capability to represent and detect vessel surfaces directly which so far has been a feature restricted to active contour and surface techniques. The key is to represent the trajectory of a tubular structure not as a 3-D curve but to go up a dimension and represent the entire structure as a 4-D curve. Then we are able to fully exploit minimal path techniques to obtain global minimizing trajectories between two user supplied endpoints in order to reconstruct tubular structures from noisy or low contrast 3-D data without the sensitivity to local minima inherent in most active surface techniques. In contrast to standard purely spatial 3-D minimal path techniques, however, we are able to represent a full tubular surface rather than just a curve which runs through its interior. Our representation also yields a natural notion of a tube's "central curve." We demonstrate and validate the utility of this approach on magnetic resonance (MR) angiography and computed tomography (CT) images of coronary arteries. View full abstract»

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  • Weighted Local Variance-Based Edge Detection and Its Application to Vascular Segmentation in Magnetic Resonance Angiography

    Page(s): 1224 - 1241
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5301 KB) |  | HTML iconHTML  

    Accurate detection of vessel boundaries is particularly important for a precise extraction of vasculatures in magnetic resonance angiography (MRA). In this paper, we propose the use of weighted local variance (WLV)-based edge detection scheme for vessel boundary detection in MRA. The proposed method is robust against changes of intensity contrast of edges and capable of giving high detection responses on low contrast edges. These robustness and capabilities are essential for detecting the boundaries of vessels in low contrast regions of images, which can contain intensity inhomogeneity, such as bias field, interferences induced from other tissues, or fluctuation of the speed related vessel intensity. The performance of the WLV-based edge detection scheme is studied and shown to be able to return strong and consistent detection responses on low contrast edges in the experiments. The proposed edge detection scheme can be embedded naturally in the active contour models for vascular segmentation. The WLV-based vascular segmentation method is tested using MRA image volumes. It is experimentally shown that the WLV-based edge detection approach can achieve high-quality segmentation of vasculatures in MRA images. View full abstract»

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  • Feature-Preserving MRI Denoising: A Nonparametric Empirical Bayes Approach

    Page(s): 1242 - 1255
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5303 KB) |  | HTML iconHTML  

    This paper presents a novel method for Bayesian denoising of magnetic resonance (MR) images that bootstraps itself by inferring the prior, i.e., the uncorrupted-image statistics, from the corrupted input data and the knowledge of the Rician noise model. The proposed method relies on principles from empirical Bayes (EB) estimation. It models the prior in a nonparametric Markov random field (MRF) framework and estimates this prior by optimizing an information-theoretic metric using the expectation-maximization algorithm. The generality and power of nonparametric modeling, coupled with the EB approach for prior estimation, avoids imposing ill-fitting prior models for denoising. The results demonstrate that, unlike typical denoising methods, the proposed method preserves most of the important features in brain MR images. Furthermore, this paper presents a novel Bayesian-inference algorithm on MRFs, namely iterated conditional entropy reduction (ICER). This paper also extends the application of the proposed method for denoising diffusion-weighted MR images. Validation results and quantitative comparisons with the state of the art in MR-image denoising clearly depict the advantages of the proposed method. View full abstract»

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  • Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies

    Page(s): 1256 - 1269
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1213 KB) |  | HTML iconHTML  

    Group studies of functional magnetic resonance imaging datasets are usually based on the computation of the mean signal across subjects at each voxel (random effects analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level are frequently violated; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives. Given that it is still very challenging to build generative or parametric models of intersubject variability, we rely on a rule based, bottom-up approach: we present a set of procedures that detect structures of interest from each subject's data, then search for correspondences across subjects and outline the most reproducible activation regions in the group studied. This framework enables a strict control on the number of false detections. It is shown here that this analysis demonstrates increased validity and improves both the sensitivity and reliability of group analyses compared with standard methods. Moreover, it directly provides information on the spatial position correspondence or variability of the activated regions across subjects, which is difficult to obtain in standard voxel-based analyses. View full abstract»

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  • Morphological Characterization of Intracranial Aneurysms Using 3-D Moment Invariants

    Page(s): 1270 - 1282
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    Rupture of intracranial saccular aneurysms is the most common cause of spontaneous subarachnoid hemorrhage, which has significant morbidity and mortality. Although there is still controversy regarding the decision on which unruptured aneurysms should be treated, this is based primarily on their size. Nonetheless, many large lesions do not rupture whereas some small ones do. It is commonly accepted that hemodynamical factors are important to better understand the natural history of cerebral aneurysms. However, it might not always be practical to carry out a detailed computational analysis of such factors if a prompt assessment is required. Since shape is likely to be dependent on the balance between hemodynamic forces and the aneurysmal surrounding environment, an appropriate morphological 3-D characterization is likely to provide a practical surrogate to quickly evaluate the risk of rupture. In this paper, an efficient and novel methodology for 3-D shape characterization of cerebral aneurysms is described. The aneurysms are isolated by taking into account a portion of their adjacent vessels. Two methods to characterize the morphology of the aneurysms models using moment invariants have been considered: geometrical moment invariants (GMI) and Zernike moment invariants (ZMI). The results have been validated in a database containing 53 patients with a total of 31 ruptured aneurysms and 24 unruptured aneurysms. It has been found that ZMI indices are more robust than GMI, and seem to provide a reliable way to discriminate between ruptured and unruptured aneurysms. Correct rupture prediction rates of sime80% were achieved in contrast to 66% that is found when the aspect ratio index is considered. View full abstract»

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  • Detection and Visualization of Surface-Pockets to Enable Phenotyping Studies

    Page(s): 1283 - 1290
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    In this paper, we propose a technique for detecting pockets on a surface-of-interest. A sequence of propagating fronts converging to the target surface is used as the basis for inspection. We compute a correspondence function between the initial and the target surface. This leads to a natural definition of the local feature size measured as the evolution distance between mapped points. Surface pockets are then extracted as salient clusters embedded in the feature space. The level-set initialization also determines the scale-space of the extracted pockets. Results are presented on a case-study in which the focus is to chronicle the phenotyping differences in genetically modified mouse placenta. Our results are validated based on manually verified ground-truth. View full abstract»

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  • Brachytherapy Seed Localization Using Geometric and Linear Programming Techniques

    Page(s): 1291 - 1304
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    We propose an optimization algorithm to solve the brachytherapy seed localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem and relies on a number of key observations which help us formulate the optimization problem as a minimization integer program (IP). Our IP model precisely defines the feasibility polyhedron for this problem using a polynomial number of half-spaces; the solution to its corresponding linear program is rounded to yield an integral solution to our task of determining correspondences between seeds in multiple projection images. The algorithm is efficient in theory as well as in practice and performs well on simulation data (~98% accuracy) and real X-ray images (~95% accuracy). We present in detail the underlying ideas and an extensive set of performance evaluations based on our implementation. View full abstract»

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  • IEEE Transactions on Medical Imaging Information for authors

    Page(s): C3
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  • Blank page [back cover]

    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.

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