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Medical Imaging, IEEE Transactions on

Issue 5 • Date May 2001

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Displaying Results 1 - 8 of 8
  • Maximum-likelihood expectation-maximization reconstruction of sinograms with arbitrary noise distribution using NEC-transformations

    Publication Year: 2001 , Page(s): 365 - 375
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (206 KB) |  | HTML iconHTML  

    The maximum-likelihood (ML) expectation-maximization (EM) [ML-EM] algorithm is being widely used for image reconstruction in positron emission tomography. The algorithm is strictly valid if the data are Poisson distributed. However, it is also often applied to processed sinograms that do not meet this requirement. This may sometimes lead to suboptimal results: streak artifacts appear and the algorithm converges toward a lower likelihood value. As a remedy, the authors propose two simple pixel-by-pixel methods [noise equivalent counts (NEC)-scaling and NEC-shifting] in order to transform arbitrary sinogram noise into noise which is approximately Poisson distributed (the first and second moments of the distribution match those of the Poisson distribution). The convergence speed associated with both transformation methods is compared, and the NEC-scaling method is validated with both simulations and clinical data. These new methods extend the ML-EM algorithm to a general purpose nonnegative reconstruction algorithm. View full abstract»

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  • Penalized discriminant analysis of [/sup 15/O]-water PET brain images with prediction error selection of smoothness and regularization hyperparameters

    Publication Year: 2001 , Page(s): 376 - 387
    Cited by:  Papers (20)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (266 KB) |  | HTML iconHTML  

    The authors propose a flexible, comprehensive approach for analysis of [ 15O]-water positron emission tomography (PET) brain images using a penalized version of linear discriminant analysis (PDA). They applied it to scans from 20 subjects (eight scans/subject) performing a finger movement task and analyzed: (1) two classes to obtain a covariance-normalized baseline-activation image, and (2) eight classes for the mean within subject temporal structure which contained baseline-activation and time-dependent changes in a two-dimensional canonical subspace. The authors imposed spatial smoothness on the resulting image(s) by expanding it in five tensor-product B-spline (TPS) bases of varying smoothness, and further regularized with a ridge-type penalty on the noise covariance matrix. The discrimination approach of PDA provides a probabilistic framework within which prediction error (PE) estimates are derived. The authors used these to optimize over TPS bases and a ridge hyperparameter (expressed as equivalent degrees of freedom, EDF). They obtained unbiased, low variance PE estimates using modern resampling tools (.632+ Bootstrap and cross validation), and compared PDA of (1) TPS-projected, mean-normalized and unnormalized scans and (2) mean-normalized scans with and without additional presmoothing. By examining the tradeoffs between PE and EDF, as a function of basis selection and image smoothing the authors demonstrate the utility of PDA, the PE framework, and the relationship between singular value decomposition and smooth TPS bases in the analysis of functional neuroimages. View full abstract»

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  • Hierarchical estimation of a dense deformation field for 3-D robust registration

    Publication Year: 2001 , Page(s): 388 - 402
    Cited by:  Papers (45)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (431 KB) |  | HTML iconHTML  

    A new method for medical image registration is formulated as a minimization problem involving robust estimators. The authors propose an efficient hierarchical optimization framework which is both multiresolution and multigrid. An anatomical segmentation of the cortex is introduced in the adaptive partitioning of the volume on which the multigrid minimization is based. This allows to limit the estimation to the areas of interest, to accelerate the algorithm, and to refine the estimation in specified areas. At each stage of the hierarchical estimation, the authors refine current estimate by seeking a piecewise affine model for the incremental deformation field. The performance of this method is numerically evaluated on simulated data and its benefits and robustness are shown on a database of 18 magnetic resonance imaging scans of the head. View full abstract»

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  • Contextual clustering for analysis of functional MRI data

    Publication Year: 2001 , Page(s): 403 - 414
    Cited by:  Papers (13)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (294 KB)  

    Presents a contextual clustering procedure for statistical parametric maps (SPM) calculated from time varying three-dimensional images. The algorithm can be used for the detection of neural activations from functional magnetic resonance images (fMRI). An important characteristic of SPM is that the intensity distribution of background (nonactive area) is known whereas the distributions of activation areas are not. The developed contextual clustering algorithm divides an SPM into background and activation areas so that the probability of detecting false activations by chance is controlled, i.e., hypothesis testing is performed. Unlike the much used voxel-by-voxel testing, neighborhood information is utilized, an important difference. This is achieved by using a Markov random field prior and iterated conditional modes (ICM) algorithm. However, unlike in the conventional use of ICM algorithm, the classification is based only on the distribution of background. The results from the authors' simulations and human fMRI experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specificity in comparison to the voxel-by-voxel thresholding technique. The algorithm is computationally efficient and can be used to detect and delineate objects from a noisy background in other applications. View full abstract»

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  • Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images

    Publication Year: 2001 , Page(s): 415 - 423
    Cited by:  Papers (86)  |  Patents (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (141 KB) |  | HTML iconHTML  

    A fully automated approach to segmentation of the left and right cardiac ventricles from magnetic resonance (MR) images is reported. A novel multistage hybrid appearance model methodology is presented in which a hybrid active shape model/active appearance model (AAM) stage helps avoid local minima of the matching function. This yields an overall more favorable matching result. An automated initialization method is introduced making the approach fully automated. The authors' method was trained in a set of 102 MR images and tested in a separate set of 60 images. In all testing cases, the matching resulted in a visually plausible and accurate mapping of the model to the image data. Average signed border positioning errors did not exceed 0.3 mm in any of the three determined contours-left-ventricular (LV) epicardium, LV and right-ventricular (RV) endocardium. The area measurements derived from the three contours correlated well with the independent standard (r=0.96, 0.96, 0.90), with slopes and intercepts of the regression lines close to one and zero, respectively. Testing the reproducibility of the method demonstrated an unbiased performance with small range of error as assessed via Bland-Altman statistic. In direct border positioning error comparison, the multistage method significantly outperformed the conventional AAM (p<0.001). The developed method promises to facilitate fully automated quantitative analysis of LV and RV morphology and function in clinical setting. View full abstract»

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  • Three-dimensional texture analysis of MRI brain datasets

    Publication Year: 2001 , Page(s): 424 - 433
    Cited by:  Papers (50)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (222 KB) |  | HTML iconHTML  

    A method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance imaging brain datasets. It is based on extended, multisort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. Basic properties of matrices as well as their sensitivity and dependence on spatial image scaling are evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets. View full abstract»

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  • Automated CT image evaluation of the lung: a morphology-based concept

    Publication Year: 2001 , Page(s): 434 - 442
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (335 KB) |  | HTML iconHTML  

    Computed tomography (CT) provides the most reliable method to detect emphysema in vivo. Commonly used methods only calculate the area of low attenuation [pixel index (PI)], while a radiologist considers the bullous morphology of emphysema. The PI is a good, well-known measure of emphysema. But it is not able to detect emphysema in cases in which emphysema and fibrosis occur at the same time. This is because fibrosis leads to a low number of low-attenuation pixels, while emphysema leads to a high number of pixels. The PI takes the average of both and, consequently, may present a result within the normal range. The main focus of this paper is to present a new algorithm of thoracic CT image evaluation based on pulmonary morphology of emphysema. The PI is extended, in that it is enabled to differentiate between small, medium, and large bullae (continuous low-attenuation areas). It is not a texture-based algorithm. The bullae are sorted by size into four size classes: class 1 being within the typical size of lung parenchyma; classes 24 presenting small, medium, and large bullae. It is calculated how much area the different classes take up of all low-attenuation pixels. The bullae index (BI) is derived from the percentage of areas covered, respectively, by small, medium, and large bullae. From the relation of the area of bullae belonging to class 4, to that of those belonging to class 2, a measure of the emphysema type (ET) is calculated. It classifies the lung by the type of emphysema in bullous emphysema or small-sized, diffuse emphysema, respectively. The BI is as reliable as the PI. In cases in which the PI indicates normal values while in fact emphysema is coexisting with fibrosis, the BI, nevertheless, detects the destruction caused by the emphysema. The BI combined with the ET reflects the visual assessment of the radiological expert. In conclusion, the BI is an objective and reliable index in order to quantify emphysematous destruction, hence, avoiding interobserv- - er variance. This is particularly interesting for follow-up. The classification of the ET is a helpful and unique approach to achieving an exact diagnosis of emphysema. View full abstract»

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  • Fractal analysis of bone X-ray tomographic microscopy projections

    Publication Year: 2001 , Page(s): 443 - 449
    Cited by:  Papers (16)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (194 KB) |  | HTML iconHTML  

    Fractal analysis of bone X-ray images has received much interest recently for the diagnosis of bone disease. Here, the authors propose a fractal analysis of bone X-ray tomographic microscopy (XTM) projections. The aim of the study is to establish whether or not there is a correlation between three-dimensional (3-D) trabecular changes and two-dimensional (2-D) fractal descriptors. Using a highly collimated beam, 3-D bone X-ray tomographic images were obtained. Trabecular bone loss was simulated using a mathematical morphology method. Then, 2-D projections were generated in each of the three orthogonal directions. Finally, the model of fractional Brownian motion (fBm) was used on bone XTM 2-D projections to characterize changes in bone structure that occur during disease, such a simulation of bone loss. Results indicate that fBm is a robust texture model allowing quantification of simulations of trabecular bone changes. 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
Michael Insana
Beckman Institute for Advanced Science and Technology
Department of Bioengineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
m.f.i@ieee.org