Showing papers in "IEEE Transactions on Medical Imaging in 2000"

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

Abstract: Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.

Interpolation revisited [medical images application]

Abstract: Based on the theory of approximation, this paper presents a unified analysis of interpolation and resampling techniques. An important issue is the choice of adequate basis functions. The authors show that, contrary to the common belief, those that perform best are not interpolating. By opposition to traditional interpolation, the authors call their use generalized interpolation; they involve a prefiltering step when correctly applied. The authors explain why the approximation order inherent in any basis function is important to limit interpolation artifacts. The decomposition theorem states that any basis function endowed with approximation order ran be expressed as the convolution of a B spline of the same order with another function that has none. This motivates the use of splines and spline-based functions as a tunable way to keep artifacts in check without any significant cost penalty. The authors discuss implementation and performance issues, and they provide experimental evidence to support their claims.

New variants of a method of MRI scale standardization

Abstract: One of the major drawbacks of magnetic resonance imaging (MRI) has been the lack of a standard and quantifiable interpretation of image intensities. Unlike in other modalities, such as X-ray computerized tomography, MR images taken for the same patient on the same scanner at different times may appear different from each other due to a variety of scanner-dependent variations and, therefore, the absolute intensity values do not have a fixed meaning. The authors have devised a two-step method wherein all images (independent of patients and the specific brand of the MR scanner used) can be transformed in such a may that for the same protocol and body region, in the transformed images similar intensities will have similar tissue meaning. Standardized images can be displayed with fixed windows without the need of per-case adjustment. More importantly, extraction of quantitative information about healthy organs or about abnormalities can be considerably simplified. This paper introduces and compares new variants of this standardizing method that can help to overcome some of the problems with the original method.

Parametric estimate of intensity inhomogeneities applied to MRI

Abstract: Presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. The authors assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further they assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. The authors present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

Imaging heart motion using harmonic phase MRI

Abstract: Describes a new image processing technique for rapid analysis and visualization of tagged cardiac magnetic resonance (MR) images. The method is based on the use of isolated spectral peaks in spatial modulation of magnetization (SPAMM)-tagged magnetic resonance images. The authors call the calculated angle of the complex image corresponding to one of these peaks a harmonic phase (HARP) image and show that HARP images can be used to synthesize conventional tag lines, reconstruct displacement fields for small motions, and calculate two-dimensional (2-D) strain. The performance of this new approach is demonstrated using both real and simulated tagged MR images. Potential for use of HARP images in fast imaging techniques and three-dimensional (3-D) analyses are discussed.

Resolution and noise properties of MAP reconstruction for fully 3-D PET

Abstract: Derives approximate analytical expressions for the local impulse response and covariance of images reconstructed from fully three-dimensional (3-D) positron emission tomography (PET) data using maximum a posteriori (MAP) estimation. These expressions explicitly account for the spatially variant detector response and sensitivity of a 3-D tomograph. The resulting spatially variant impulse response and covariance are computed using 3-D Fourier transforms. A truncated Gaussian distribution is used to account for the effect on the variance of the nonnegativity constraint used in MAP reconstruction. Using Monte Carlo simulations and phantom data from the microPET small animal scanner, the authors show that the approximations provide reasonably accurate estimates of contrast recovery and covariance of MAP reconstruction for priors with quadratic energy functions. They also describe how these analytical results can be used to achieve near-uniform contrast recovery throughout the reconstructed volume.

A multiscale dynamic programming procedure for boundary detection in ultrasonic artery images

Abstract: Ultrasonic measurements of human carotid and femoral artery walls are conventionally obtained by manually tracing interfaces between tissue layers. The drawbacks of this method are the interobserver variability and inefficiency. Here, the authors present a new automated method which reduces these problems. By applying a multiscale dynamic programming (DP) algorithm, approximate vessel wall positions are first estimated in a coarse-scale image, which then guide the detection of the boundaries in a fine-scale image. In both cases, DP is used for finding a global optimum for a cost function. The cost function is a weighted sum of terms, in fuzzy expression forms, representing image features and geometrical characteristics of the vessel interfaces. The weights are adjusted by a training procedure using human expert tracings. Operator interventions, if needed, also take effect under the framework of global optimality. This reduces the amount of human intervention and, hence, variability due to subjectiveness. By incorporating human knowledge and experience, the algorithm becomes more robust. A thorough evaluation of the method in the clinical environment shows that interobserver variability is evidently decreased and so is the overall analysis time. The authors conclude that the automated procedure can replace the manual procedure and leads to an improved performance.

A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films

Abstract: Clusters of microcalcifications in mammograms are an important early sign of breast cancer. This paper presents a computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms. The proposed system consists of two main steps. First, potential microcalcification pixels in the mammograms are segmented out by using mixed features consisting of wavelet features and gray level statistical features, and labeled into potential individual microcalcification objects by their spatial connectivity. Second, individual microcalcifications are detected by using a set of 31 features extracted from the potential individual microcalcification objects. The discriminatory power of these features is analyzed using general regression neural networks via sequential forward and sequential backward selection methods. The classifiers used in these two steps are both multilayer feedforward neural networks. The method is applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications. A free-response operating characteristics (FROC) curve is used to evaluate the performance. Results show that the proposed system gives quite satisfactory detection performance. In particular, a 90% mean true positive detection rate is achieved at the cost of 0.5 false positive per image when mixed features are used in the first step and 15 features selected by the sequential backward selection method are used in the second step. However, one must be cautious when interpreting the results, since the 20 training samples are also used in the testing step.

Voxel similarity measures for 3-D serial MR brain image registration

Abstract: The authors have evaluated eight different similarity measures used for rigid body registration of serial magnetic resonance (MR) brain scans. To assess their accuracy the authors used 33 clinical three-dimensional (3-D) serial MR images, with deformable extradural tissue excluded by manual segmentation and simulated 3-D MR images with added intensity distortion. For each measure the authors determined the consistency of registration transformations for both sets of segmented and unsegmented data. They have shown that of the eight measures tested, the ones based on joint entropy produced the best consistency. In particular, these measures seemed to be least sensitive to the presence of extradural tissue. For these data the difference in accuracy of these joint entropy measures, with or without brain segmentation, was within the threshold of visually detectable change in the difference images.

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

Abstract: The authors have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, they have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. Here, they introduce an ultra-fast live-wire method, referred to as live wire on the fly for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently, a live wire is displayed in real time from the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is found next. The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, the authors solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, the authors demonstrate that live wire on the fly is 1.331 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.

Gradient and texture analysis for the classification of mammographic masses

Abstract: Computer-aided classification of benign and malignant masses on mammograms is attempted in this study by computing gradient-based and texture-based features. Features computed based on gray-level co-occurrence matrices (GCMs) are used to evaluate the effectiveness of textural information possessed by mass regions in comparison with the textural information present in mass margins. A method involving polygonal modeling of boundaries is proposed for the extraction of a ribbon of pixels across mass margins. Two gradient-based features are developed to estimate the sharpness of mass boundaries in the ribbons of pixels extracted from their margins. A total of 54 images (28 benign and 26 malignant) containing 39 images from the Mammographic Image Analysis Society (MIAS) database and 15 images from a local database are analyzed. The best benign versus malignant classification of 82.1%, with an area (A/sub z/) of 0.85 under the receiver operating characteristics (ROC) curve, was obtained with the images from the MIAS database by using GCM-based texture features computed from mass margins. The classification method used is based on posterior probabilities computed from Mahalanobis distances. The corresponding accuracy using jack-knife classification was observed to be 74.4%, with A/sub x/=0.67. Gradient-based features achieved A/sub x/=0.6 on the MIAS database and A/sub z/=0.76 on the combined database. The corresponding values obtained using jack-knife classification were observed to be 0.52 and 0.73 for the MIAS and combined databases, respectively.

Fast delineation and visualization of vessels in 3-D angiographic images

Abstract: A method is presented which aids the clinician in obtaining quantitative measures and a three-dimensional (3-D) representation of vessels from 3-D angiographic data with a minimum of user interaction. Based on two user defined starting points, an iterative procedure tracks the central vessel axis. During the tracking process, the minimum diameter and a surface rendering of the vessels are computed, allowing for interactive inspection of the vasculature. Applications of the method to CTA, contrast enhanced (CE)-MRA and phase contrast (PC)-MRA images of the abdomen are shown. In all applications, a long stretch of vessels with varying width is tracked, delineated, and visualized, in less than 10 s on a standard clinical workstation.

X-ray CT metal artifact reduction using wavelets: an application for imaging total hip prostheses

Abstract: Traditional computed tomography (CT) reconstructions of total joint prostheses are limited by metal artifacts from corrupted projection data. Published metal artifact reduction methods are based on the assumption that severe attenuation of X-rays by prostheses renders corresponding portions of projection data unavailable, hence the "missing" data are either avoided (in iterative reconstruction) or interpolated (in filtered back-projection with data completion; typically, with filling data "gaps" via linear functions). Here, the authors propose a wavelet-based multiresolution analysis method for metal artifact reduction, in which information is extracted from corrupted projection data. The wavelet method improves image quality by a successive interpolation in the wavelet domain. Theoretical analysis and experimental results demonstrate that the metal artifacts due to both photon starving and beam hardening can be effectively suppressed using the authors' method. As compared to the filtered back-projection after linear interpolation, the wavelet-based reconstruction is significantly more accurate for depiction of anatomical structures, especially in the immediate neighborhood of the prostheses. This superior imaging precision is highly advantageous in geometric modeling for fitting hip prostheses.

Unmatched projector/backprojector pairs in an iterative reconstruction algorithm

Abstract: Computational burden is a major concern when an iterative algorithm is used to reconstruct a three-dimensional (3-D) image with attenuation, detector response, and scatter corrections. Most of the computation time is spent executing the projector and backprojector of an iterative algorithm. Usually, the projector and the backprojector are transposed operators of each other. The projector should model the imaging geometry and physics as accurately as possible. Some researchers have used backprojectors that are computationally less expensive than the projectors to reduce computation time. This paper points out that valid backprojectors should satisfy a condition that the projector/backprojector matrix must not contain negative eigenvalues. This paper also investigates the effects when unmatched projector/backprojector pairs are used.

Unwarping of unidirectionally distorted EPI images

Abstract: Echo-planar imaging (EPI) is a fast nuclear magnetic resonance imaging (MRI) method. Unfortunately, local magnetic field inhomogeneities induced mainly by the subject's presence cause significant geometrical distortion, predominantly along the phase-encoding direction, which must be undone to allow for meaningful further processing. So far, this aspect has been too often neglected. In this paper, the authors suggest a new approach using an algorithm specifically developed for the automatic registration of distorted EPI images with corresponding anatomically correct MRI images. They model the deformation field with splines, which gives us a great deal of flexibility, while comprising the affine transform as a special case. The registration criterion is least squares. Interestingly, the complexity of its evaluation does not depend on the resolution of the control grid. The spline model gives the authors good accuracy thanks to its high approximation order. The short support of splines leads to a fast algorithm. A multiresolution approach yields robustness and additional speedup. The algorithm was tested on real as well as synthetic data, and the results were compared with a manual method. A wavelet-based Sobolev-type random deformation generator was developed for testing purposes. A blind test indicates that the proposed automatic method is faster, more reliable, and more precise than the manual one.

Modeling the hemodynamic response in fMRI using smooth FIR filters

Abstract: Modeling the hemodynamic response in functional magnetic resonance (fMRI) experiments is an important aspect of the analysis of functional neuroimages. This has been done in the past using parametric response function, from a limited family. In this contribution, the authors adopt a semi-parametric approach based on finite impulse response (FIR) filters. In order to cope with the increase in the number of degrees of freedom, the authors introduce a Gaussian process prior on the filter parameters. They show how to carry on the analysis by incorporating prior knowledge on the filters, optimizing hyper-parameters using the evidence framework, or sampling using a Markov Chain Monte Carlo (MCMC) approach. The authors present a comparison of their model with standard hemodynamic response kernels on simulated data, and perform a full analysis of data acquired during an experiment involving visual stimulation.

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

Abstract: This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects-shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. The authors' method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.

Design and evaluation of a system for microscope-assisted guided interventions (MAGI)

Abstract: The problem of providing surgical navigation using image overlays on the operative scene can be split into four main tasks-calibration of the optical system; registration of preoperative images to the patient; system and patient tracking, and display using a suitable visualization scheme To achieve a convincing result in the magnified microscope view a very high alignment accuracy is required The authors have simulated an entire image overlay system to establish the most significant sources of error and improved each of the stages involved The microscope calibration process has been automated The authors have introduced bone-implanted markers for registration and incorporated a locking acrylic dental stent (LADS) for patient tracking The LADS can also provide a less-invasive registration device with mean target error of 07 mm in volunteer experiments These improvements have significantly increased the alignment accuracy of the authors' overlays Phantom accuracy is 03-05 mm and clinical overlay errors were 05-10 mm on the bone fiducials and 05-4 mm on target structures The authors have improved the graphical representation of the stereo overlays The resulting system provides three-dimensional surgical navigation for microscope-issisted guided interventions (MAGI)

Enhanced 3-D-reconstruction algorithm for C-arm systems suitable for interventional procedures

Abstract: Increasingly, three dimensional (3-D) imaging technologies are used in medical diagnosis, for therapy planning, and during interventional procedures. The authors describe the possibilities of fast 3-D-reconstruction of high-contrast objects with high spatial resolution from only a small series of two-dimensional (2-D) planar radiographs. The special problems arising from the intended use of an open, mechanically unstable C-arm system are discussed. For the description of the irregular sampling geometry, homogeneous coordinates are used thoroughly. The well-known Feldkamp algorithm is modified to incorporate corresponding projection matrices without any decomposition into intrinsic and extrinsic parameters. Some approximations to speed up the whole reconstruction procedure and the tradeoff between image quality and computation time are also considered. Using standard hardware the reconstruction of a 256/sup 3/ cube is now possible within a few minutes, a time that is acceptable during interventions. Examples for cranial vessel imaging from some clinical test installations will be shown as well as promising results for bone imaging with a laboratory C-arm system.

Nondistorting flattening maps and the 3-D visualization of colon CT images

Abstract: Considers a novel three-dimensional (3-D) visualization technique based on surface flattening for virtual colonoscopy. Such visualization methods could be important in virtual colonoscopy because they have the potential for noninvasively determining the presence of polyps and other pathologies. Further, the authors demonstrate a method that presents a surface scan of the entire colon as a cine, and affords the viewer the opportunity to examine each point on the surface without distortion. The authors use certain angle-preserving mappings from differential geometry to derive an explicit method for flattening surfaces obtained from 3-D colon computed tomography (CT) imagery. Indeed, the authors describe a general method based on a discretization of the Laplace-Beltrami operator for flattening a surface into the plane in a conformal manner. From a triangulated surface representation of the colon, the authors indicate how the procedure may be implemented using a finite element technique, which takes into account special boundary conditions. They also provide simple formulas that may be used in a real-time cine to correct for distortion.

3-D reconstruction of coronary arterial tree to optimize angiographic visualization

Abstract: Due to vessel overlap and foreshortening, multiple projections are necessary to adequately evaluate the coronary tree with arteriography. Catheter-based interventions can only be optimally performed when these visualization problems are successfully solved. The traditional method provides multiple selected views in which overlap and foreshortening are subjectively minimized based on two dimensional (2-D) projections. A pair of images acquired from routine angiographic study at arbitrary orientation using a single-plane imaging system were chosen for three-dimensional (3-D) reconstruction. After the arterial segment of interest (e.g., a single coronary stenosis or bifurcation lesion) was selected, a set of gantry angulations minimizing segment foreshortening was calculated. Multiple computer-generated projection images with minimized segment foreshortening were then used to choose views with minimal overlapped vessels relative to the segment of interest. The optimized views could then be utilized to guide subsequent angiographic acquisition and interpretation. Over 800 cases of coronary arterial trees have been reconstructed, in which more than 40 cases were performed in room during cardiac catheterization. The accuracy of 3-D length measurement was confirmed to be within an average root-mean-square (rms) 3.5% error using eight different pairs of angiograms of an intracoronary guidewire of 105-mm length with eight radiopaque markers of 15-mm interdistance. The accuracy of similarity between the additional computer-generated projections versus the actual acquired views was demonstrated with the average rms errors of 3.09 mm and 3.13 mm in 20 LCA and 20 RCA cases, respectively. The projections of the reconstructed patient-specific 3-D coronary tree model can be utilized for planning optimal clinical views: minimal overlap and foreshortening. The assessment of lesion length and diameter narrowing can be optimized in both interventional cases and studies of disease progression and regression.

Regularization for uniform spatial resolution properties in penalized-likelihood image reconstruction

Abstract: Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space variant, asymmetric, and object-dependent even for space invariant imaging systems. The authors propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. The authors have developed computationally efficient methods for PET systems with shift-invariant geometric responses. They demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.

Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware

Abstract: Algebraic reconstruction methods, such as the algebraic reconstruction technique (ART) and the related simultaneous ART (SART), reconstruct a two-dimensional (2-D) or three-dimensional (3-D) object from its X-ray projections. The algebraic methods have, in certain scenarios, many advantages over the more popular Filtered Backprojection approaches and have also recently been shown to perform well for 3-D cone-beam reconstruction. However, so far the slow speed of these iterative methods have prohibited their routine use in clinical applications. Here, the authors address this shortcoming and investigate the utility of widely available 2-D texture mapping graphics hardware for the purpose of accelerating the 3-D algebraic reconstruction. They find that this hardware allows 3-D cone-beam reconstructions to be obtained at almost interactive speeds, with speed-ups of over 50 with respect to implementations that only use general-purpose CPUs. However the authors also find that the reconstruction quality is rather sensitive to the resolution of the framebuffer, and to address this critical issue they propose a scheme that extends the precision of a given framebuffer by 4 bits, using the color channels. With this extension, a 12-bit framebuffer delivers useful reconstructions for 0.5% tissue contrast, while an 8-bit framebuffer requires 4%. Since graphics hardware generates an entire image for each volume projection, it is most appropriately used with an algebraic reconstruction method that performs volume correction at that granularity as well, such as SART or SIRT. The authors chose SART for its faster convergence properties.

Penalized weighted least-squares image reconstruction for dual energy X-ray transmission tomography

Abstract: Presents a dual-energy (DE) transmission computed tomography (CT) reconstruction method It is statistically motivated and features nonnegativity constraints in the density domain A penalized weighted least squares (PWLS) objective function has been chosen to handle the non-Poisson noise added by amorphous silicon (aSi:H) detectors A Gauss-Seidel algorithm has been used to minimize the objective function The behavior of the method in terms of bias/standard deviation tradeoff has been compared to that of a DE method that is based on filtered back projection (FBP) The advantages of the DE PWLS method are largest for high noise and/or low flux cases Qualitative results suggest this as well Also, the reconstructed images of an object with opaque regions are presented Possible applications of the method are: attenuation correction for positron emission tomography (PET) images, various quantitative computed tomography (QCT) methods such as bone mineral densitometry (BMD), and the removal of metal streak artifacts

Edge-guided boundary delineation in prostate ultrasound images

Abstract: Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. Here, a new paradigm for guided edge delineation is described, a which involves presenting automatically detected prostate edges as a visual guide to the observer, followed by manual editing. This approach enables robust delineation of the prostate boundaries, making it suitable for routine clinical use. The edge-detection algorithm is comprised of three stages. An algorithm called sticks is used to enhance contrast and at the same time reduce speckle in the transrectal ultrasound prostate image. The resulting image is further smoothed using an anisotropic diffusion filter. In the third stage, some basic prior knowledge of the prostate, such as shape and echo pattern, is used to detect the most probable edges describing the prostate. Finally, patient-specific anatomic information is integrated during manual linking of the detected edges. The algorithm was tested on 125 images from 16 patients. The performance of the algorithm was statistically evaluated by employing five expert observers. Based on this study, the authors found that consistency in prostate delineation increases when automatically detected edges are used as visual guide during outlining, while the accuracy of the detected edges was found to be at least as good as those of the human observers. The use of edge guidance for boundary delineation can also be extended to other applications in medical imaging where poor contrast in the images and the complexity in the anatomy limit the clinical usability of fully automatic edge-detection techniques.

Accurate alignment of functional EPI data to anatomical MRI using a physics-based distortion model

Abstract: Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. The authors make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. They also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a magnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.

List-mode maximum-likelihood reconstruction applied to positron emission mammography (PEM) with irregular sampling

Abstract: Presents a preliminary study of list-mode likelihood reconstruction of images for a rectangular positron emission tomograph (PET) specifically designed to image the human breast. The prospective device consists of small arrays of scintillation crystals for which depth of interaction is estimated. Except in very rare instances, the number of annihilation events detected is expected to be far less than the number of distinguishable events. If one were to histogram the acquired data, most histogram bins would remain vacant. Therefore, it seems natural to investigate the efficacy of processing events one at a time rather than processing the data in histogram format. From a reconstruction perspective, the new tomograph presents a challenge in that the rectangular geometry leads to irregular radial and angular sampling, and the field of view extends completely to the detector faces. Simulations are presented that indicate that the proposed tomograph can detect 8-mm-diameter spherical tumors with a tumor-to-background tracer density ratio of 3:1 using realistic image acquisition parameters. Spherical tumors of 4-mm diameter are near the limit of detectability with the image acquisition parameters used. Expressions are presented to estimate the loss of image contrast due to Compton scattering.

Texture analysis for classification of cervix lesions

Abstract: This paper presents a generalized statistical texture analysis technique for characterizing and recognizing typical, diagnostically most important, vascular patterns relating to cervical lesions from colposcopic images. The contributions of the research include: (1) the introduction of a generalized texture analysis technique based on the combination of the conventional statistical and structural textural analysis approaches by using a statistical description of geometric primitives; (2) the introduction of a set of textural measures that capture the specific characteristics of cervical textures as perceived by humans. An experimental study with real images demonstrated the feasibility and promise of the proposed approach in discriminating between cervical texture patterns indicative of different stages of cervical lesions.

Brain tissue classification of magnetic resonance images using partial volume modeling

Abstract: Presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, the authors consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk or of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: (1) segmentation of the brain into pure and mixclasses using the mixture model; (2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.

Flat panel detector-based cone-beam volume CT angiography imaging: system evaluation

Abstract: Preliminary evaluation of recently developed large-area flat panel detectors (FPDs) indicates that FPDs have some potential advantages: compactness, absence of geometric distortion and veiling glare with the benefits of high resolution, high detective quantum efficiency (DQE), high frame rate and high dynamic range, small image lag (<1%), and excellent linearity (/spl sim/1%). The advantages of the new FPD make it a promising candidate for cone-beam volume computed tomography (CT) angiography (CBVCTA) imaging. The purpose of this study is to characterize a prototype FPD-based imaging system for CBVCTA applications. A prototype FPD-based CBVCTA imaging system has been designed and constructed around a modified GE 8800 CT scanner. This system is evaluated for a CBVCTA imaging task in the head and neck using four phantoms and a frozen rat. The system is first characterized in terms of linearity and dynamic range of the detector. Then, the optimal selection of kVps for CBVCTA is determined and the effect of image lag and scatter on the image quality of the CBVCTA system is evaluated. Next, low-contrast resolution and high-contrast spatial resolution are measured. Finally, the example reconstruction images of a frozen rat are presented. The results indicate that the FPD-based CBVCT can achieve 2.75-1p/mm spatial resolution at 0% modulation transfer function (MTF) and provide more than enough low-contrast resolution for intravenous CBVCTA imaging in the head and neck with clinically acceptable entrance exposure level. The results also suggest that to use an FPD for large cone-angle applications, such as body angiography, further investigations are required.