Segmentation of 3D intravascular ultrasonic images based on a random field model

A Review on Atherosclerotic Biology, Wall Stiffness, Physics of Elasticity, and Its Ultrasound-Based Measurement

Functional and structural changes in the common carotid artery are biomarkers for cardiovascular risk. Current methods for measuring functional changes include pulse wave velocity, compliance, distensibility, strain, stress, stiffness, and elasticity derived from arterial waveforms. The review is focused on the ultrasound-based carotid artery elasticity and stiffness measurements covering the physics of elasticity and linking it to biological evolution of arterial stiffness. The paper also presents evolution of plaque with a focus on the pathophysiologic cascade leading to arterial hardening. Using the concept of strain, and image-based elasticity, the paper then reviews the lumen diameter and carotid intima-media thickness measurements in combined temporal and spatial domains. Finally, the review presents the factors which influence the understanding of atherosclerotic disease formation and cardiovascular risk including arterial stiffness, tissue morphological characteristics, and image-based elasticity measurement.

Methods for 2-D and 3-D Endobronchial Ultrasound Image Segmentation

Endobronchial ultrasound (EBUS) is now commonly used for cancer-staging bronchoscopy. Unfortunately, EBUS is challenging to use and interpreting EBUS video sequences is difficult. Other ultrasound imaging domains, hampered by related difficulties, have benefited from computer-based image-segmentation methods. Yet, so far, no such methods have been proposed for EBUS. We propose image-segmentation methods for 2-D EBUS frames and 3-D EBUS sequences. Our 2-D method adapts the fast-marching level-set process, anisotropic diffusion, and region growing to the problem of segmenting 2-D EBUS frames. Our 3-D method builds upon the 2-D method while also incorporating the geodesic level-set process for segmenting EBUS sequences. Tests with lung-cancer patient data showed that the methods ran fully automatically for nearly 80% of test cases. For the remaining cases, the only user-interaction required was the selection of a seed point. When compared to ground-truth segmentations, the 2-D method achieved an overall Dice index = 90.0% ±4.9%, while the 3-D method achieved an overall Dice index = 83.9 ± 6.0%. In addition, the computation time (2-D, 0.070 s/frame; 3-D, 0.088 s/frame) was two orders of magnitude faster than interactive contour definition. Finally, we demonstrate the potential of the methods for EBUS localization in a multimodal image-guided bronchoscopy system.

Computer Vision Techniques for Transcatheter Intervention

Minimally invasive transcatheter technologies have demonstrated substantial promise for the diagnosis and the treatment of cardiovascular diseases. For example, transcatheter aortic valve implantation is an alternative to aortic valve replacement for the treatment of severe aortic stenosis, and transcatheter atrial fibrillation ablation is widely used for the treatment and the cure of atrial fibrillation. In addition, catheter-based intravascular ultrasound and optical coherence tomography imaging of coronary arteries provides important information about the coronary lumen, wall, and plaque characteristics. Qualitative and quantitative analysis of these cross-sectional image data will be beneficial to the evaluation and the treatment of coronary artery diseases such as atherosclerosis. In all the phases (preoperative, intraoperative, and postoperative) during the transcatheter intervention procedure, computer vision techniques (e.g., image segmentation and motion tracking) have been largely applied in the field to accomplish tasks like annulus measurement, valve selection, catheter placement control, and vessel centerline extraction. This provides beneficial guidance for the clinicians in surgical planning, disease diagnosis, and treatment assessment. In this paper, we present a systematical review on these state-of-the-art methods. We aim to give a comprehensive overview for researchers in the area of computer vision on the subject of transcatheter intervention. Research in medical computing is multi-disciplinary due to its nature, and hence, it is important to understand the application domain, clinical background, and imaging modality, so that methods and quantitative measurements derived from analyzing the imaging data are appropriate and meaningful. We thus provide an overview on the background information of the transcatheter intervention procedures, as well as a review of the computer vision techniques and methodologies applied in this area.

A state-of-the-art review on segmentation algorithms in intravascular ultrasound (IVUS) images

Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.

Segmentation in ultrasonic B-mode images of healthy carotid arteries using mixtures of Nakagami distributions and stochastic optimization

The goal of this work is to perform a segmentation of the intimamedia thickness (IMT) of carotid arteries in view of computing various dynamical properties of that tissue, such as the elasticity distribution (elastogram). The echogenicity of a region of interest comprising the intima-media layers, the lumen, and the adventitia in an ultrasonic B-mode image is modeled by a mixture of three Nakagami distributions. In a first step, we compute the maximum a posteriori estimator of the proposed model, using the expectation maximization (EM) algorithm. We then compute the optimal segmentation based on the estimated distributions as well as a statistical prior for disease-free IMT using a variant of the exploration/selection (ES) algorithm. Convergence of the ES algorithm to the optimal solution is assured asymptotically and is independent of the initial solution. In particular, our method is well suited to a semi-automatic context that requires minimal manual initialization. Tests of the proposed method on 30 sequences of ultrasonic B-mode images of presumably disease-free control subjects are reported. They suggest that the semi-automatic segmentations obtained by the proposed method are within the variability of the manual segmentations of two experts.

Cell-based two-region competition algorithm with a map framework for boundary delineation of a series of 2D ultrasound images

To ensure the delineated boundaries of a series of 2-D images closely following the visually perceivable edges with high boundary coherence between consecutive slices, a cell-based two-region competition algorithm based on a maximum a posteriori (MAP) framework is proposed. It deforms the region boundary in a cell-by-cell fashion through a cell-based two-region competition process. The cell-based deformation is guided by a cell-based MAP framework with a posterior function characterizing the distribution of the cell means in each region, the salience and shape complexity of the region boundary and the boundary coherence of the consecutive slices. The proposed algorithm has been validated using 10 series of breast sonograms, including seven compression series and three freehand series. The compression series contains two carcinoma and five fibroadenoma cases and the freehand series contains two carcinoma and one fibroadenoma cases. The results show that >70% of the derived boundaries fall within the span of the manually delineated boundaries. The robustness of the proposed algorithm to the variation of regions-of-interest is confirmed by the Friedman tests and the p-values of which are 0.517 and 0.352 for the compression and freehand series groups, respectively. The Pearson's correlations between the lesion sizes derived by the proposed algorithm and those defined by the average manually delineated boundaries are all higher than 0.990. The overlapping and difference ratios between the derived boundaries and the average manually delineated boundaries are mostly higher than 0.90 and lower than 0.13, respectively. For both series groups, all assessments conclude that the boundaries derived by the proposed algorithm be comparable to those delineated manually. Moreover, it is shown that the proposed algorithm is superior to the Chan and Vese level set method based on the paired-sample t-tests on the performance indices at a 5% significance level.

Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

Statistical strategy for anisotropic adventitia modelling in IVUS

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions

Intravascular ultrasound (IVUS) is a catheter based medical imaging technique particularly useful for studying atherosclerotic disease. It produces cross-sectional images of blood vessels that provide quantitative assessment of the vascular wall, information about the nature of atherosclerotic lesions as well as plaque shape and size. Automatic processing of large IVUS data sets represents an important challenge due to ultrasound speckle, catheter artifacts or calcification shadows. A new three-dimensional (3-D) IVUS segmentation model, that is based on the fast-marching method and uses gray level probability density functions (PDFs) of the vessel wall structures, was developed. The gray level distribution of the whole IVUS pullback was modeled with a mixture of Rayleigh PDFs. With multiple interface fast-marching segmentation, the lumen, intima plus plaque structure, and media layers of the vessel wall were computed simultaneously. The PDF-based fast-marching was applied to 9 in vivo IVUS pullbacks of superficial femoral arteries and to a simulated IVUS pullback. Accurate results were obtained on simulated data with average point to point distances between detected vessel wall borders and ground truth <0.072 mm. On in vivo IVUS, a good overall performance was obtained with average distance between segmentation results and manually traced contours <0.16 mm. Moreover, the worst point to point variation between detected and manually traced contours stayed low with Hausdorff distances <0.40 mm, indicating a good performance in regions lacking information or containing artifacts. In conclusion, segmentation results demonstrated the potential of gray level PDF and fast-marching methods in 3-D IVUS image processing.

A level set framework with a shape and motion prior for segmentation and region tracking in echocardiography

We describe a level set formulation using both shape and motion prior, for both segmentation and region tracking in high frame rate echocardiographic image sequences. The proposed approach uses the following steps: registration of the prior shape, level set segmentation constrained through the registered shape and region tracking. Registration of the prior shape is expressed as a rigid or an affine transform problem, where the transform minimizing a global region-based criterion is sought. This criterion is based on image statistics and on the available estimated axial motion data. The segmentation step is then formulated through front propagation, constrained with the registered shape prior. The same region-based criterion is used both for the registration and the segmentation step. Region tracking is based on the motion field estimated from the interframe level set evolution. The proposed approach is applied to high frame rate echocardiographic sequences acquired in vivo. In this particular application, the prior shape is provided by a medical expert and the rigid transform is used for registration. It is shown that this approach provides consistent results in terms of segmentation and stability through the cardiac cycle. In particular, a comparison indicates that the results provided by our approach are very close to the results obtained with manual tracking performed by an expert cardiologist on a Doppler Tissue Imaging (DTI) study. These preliminary results show the ability of the method to perform region tracking and its potential for dynamic parametric imaging of the heart.

Segmentation of elastographic images using a coarse-to-fine active contour model

Delineation of radiofrequency-ablation-induced coagulation (thermal lesion) boundaries is an important clinical problem that is not well addressed by conventional imaging modalities. Elastography, which produces images of the local strain after small, externally applied compressions, can be used for visualization of thermal coagulations. This paper presents an automated segmentation approach for thermal coagulations on 3-D elastographic data to obtain both area and volume information rapidly. The approach consists of a coarse-to-fine method for active contour initialization and a gradient vector flow, active contour model for deformable contour optimization with the help of prior knowledge of the geometry of general thermal coagulations. The performance of the algorithm has been shown to be comparable to manual delineation of coagulations on elastograms by medical physicists (r = 0.99 for volumes of 36 radiofrequency-induced coagulations). Furthermore, the automatic algorithm applied to elastograms yielded results that agreed with manual delineation of coagulations on pathology images (r = 0.96 for the same 36 lesions). This algorithm has also been successfully applied on in vivo elastograms.

Evaluation of an algorithm for semiautomated segmentation of thin tissue layers in high-frequency ultrasound images

An algorithm consisting of speckle reduction by median filtering, contrast enhancement using top- and bottom-hat morphological filters, and segmentation with a discrete dynamic contour (DDC) model was implemented for nondestructive measurements of soft tissue layer thickness. Algorithm performance was evaluated by segmenting simulated images of three-layer phantoms and high-frequency (40 MHz) ultrasound images of porcine aortic valve cusps in vitro. The simulations demonstrated the necessity of the median and morphological filtering steps and enabled testing of user-specified parameters of the morphological filters and DDC model. In the experiments, six cusps were imaged in coronary perfusion solution (CPS) then in distilled water to test the algorithm's sensitivity to changes in the dimensions of thin tissue layers. Significant increases in the thickness of the fibrosa, spongiosa, and ventricularis layers, by 53.5% (p < 0.001), 88.5% (p < 0.001), and 35.1% (p = 0.033), respectively, were observed when the specimens were submerged in water. The intraobserver coefficient of variation of repeated thickness estimates ranged from 0.044 for the fibrosa in water to 0.164 for the spongiosa in CPS. Segmentation accuracy and variability depended on the thickness and contrast of the layers, but the modest variability provides confidence in the thickness measurements.

Segmentation in Echocardiographic Sequences using Shape-based Snake Model Combined with Generalized Hough Transformation

A novel method for segmentation of cardiac structures in temporal echocardiographic sequences based on the snake model is presented. The method is motivated by the observation that the structures of neighboring frames have consistent locations and shapes that aid in segmentation. To cooperate with the constraining information provided by the neighboring frames, we combine the template matching with the conventional snake model. It means that the model not only is driven by conventional internal and external forces, but also combines an additional constraint, the matching degree to measure the similarity between the neighboring prior shape and the derived contour. Furthermore, in order to auto or semi-automatically segment the sequent images without manually drawing the initial contours in each image, generalized Hough transformation (GHT) is used to roughly estimate the initial contour by transforming the neighboring prior shape. The method is particularly useful in case of the large frame-to-frame displacement of structure such as mitral valve. As a result, the active contour can easily detect the desirable boundaries in ultrasound images and has a high penetrability through the interference of various undesirables, such as the speckle, the tissue-related textures and the artifacts.

Automatic 3D Segmentation of Intravascular Ultrasound Images Using Region and Contour Information

Intravascular ultrasound (IVUS) produces images of arteries that show the lumen in addition to the layered structure of the vessel wall. A new automatic 3D IVUS fast-marching segmentation model is presented. The method is based on a combination of region and contour information, namely the gray level probability density functions (PDFs) of the vessel structures and the image gradient. Accurate results were obtained on in-vivo and simulated data with average point to point distances between detected vessel wall boundaries and validation contours below 0.105 mm. Moreover, Hausdorff distances (that represent the worst point to point variations) resulted in values below 0.344 mm, which demonstrate the potential of combining region and contour information in a fast-marching scheme for 3D automatic IVUS image processing.

Real time computation and temporal coherence of opacity transfer functions for direct volume rendering of ultrasound data

Opacity transfer function (OTF) generation for direct volume rendering of medical image data is an intensely discussed subject. Several automatic methods exist for CT and MRI data, which are not apt for ultrasound data, mainly due to its low signal-to-noise ratio. Furthermore, ultrasound (US) imaging is able to produce time-varying 3D datasets in real time thus opening the door to 4D visualization. However, OTF design for 4D datasets has not been exhaustively discussed until now. We present an efficient solution to generate an optimized OTF for a given 3DUS dataset in real time. Our method results in excellent visualization which we demonstrate using 3D fetus datasets. Finally, we discuss the applicability of our method to 4DUS visualization.

An Automated Method for Lumen and Media–Adventitia Border Detection in a Sequence of IVUS Frames

In this paper, we present a method for the automated detection of lumen and media-adventitia border in sequential intravascular ultrasound (IVUS) frames. The method is based on the use of deformable models. The energy function is appropriately modified and minimized using a Hopfield neural network. Proper modifications in the definition of the bias of the neurons have been introduced to incorporate image characteristics. A simulated annealing scheme is included to ensure convergence at a global minimum. The method overcomes distortions in the expected image pattern, due to the presence of calcium, employing a specialized structure of the neural network and boundary correction schemas which are based on a priori knowledge about the vessel geometry. The proposed method is evaluated using sequences of IVUS frames from 18 arterial segments, some of them indicating calcified regions. The obtained results demonstrate that our method is statistically accurate, reproducible, and capable to identify the regions of interest in sequences of IVUS frames.

Virtual 3D IVUS vessel model for intravascular brachytherapy planning. I. 3D segmentation, reconstruction, and visualization of coronary artery architecture and orientation

Intravascular brachytherapy (IVB) can significantly reduce the risk of restenosis after interventional treatment of stenotic arteries, if planned and applied correctly. To facilitate computer-based IVB planning, a three-dimensional vessel model has been derived from information on coronary artery segments acquired by intravascular ultrasound (IVUS) and biplane angiography. Part I describes the approach of model construction and presents possibilities of visualization. The vessel model is represented by a voxel volume. Polygonal information about the vessel wall structure is derived by segmentation from a sequence of IVUS images automatically acquired ECG gated during pull back of the IVUS transducer. To detect horizontal, vertical, and radial contours, modified Canny-Edge and Shen-Castan filters are applied on Cartesian and polar coordinate representations of the IVUS tomograms as edge detectors. The spatial course of the vessel wall layers is traced in reconstructed longitudinal IVUS scans. By resampling the sequence of IVUS frames the voxel volume is obtained. For this purpose the frames are properly located in space and augmented with additional intermediate frames generated by interpolation. Their spatial location and orientation is derived from biplane X-ray angiography which is performed simultaneously. For resampling, two approaches are proposed: insertion of the vertices of the rectangular goal grid into the cells of a deformed hexahedral mesh derived from the IVUS sequence, and insertion of the vertices of the hexahedral mesh into the cells of the rectangular grid. Finally, the vessel model is visualized by methods of combined volume and polygon rendering. The segmentation process is verified as being in good agreement with results obtained by manual contour tracing with a commercial system. Our approach of construction of the vessel model has been implemented into an interactive software system, 3D IVUS-View, serving as the basis of a future system for intracoronary brachytherapy treatment planning being currently under development (Part II).

Fully automatic luminal contour segmentation in intracoronary ultrasound imaging--a statistical approach

In this paper, a fully automatic method for luminal contour segmentation in intracoronary ultrasound imaging is introduced. Its principle is based on a contour with a priori properties that evolves according to the statistics of the ultrasound texture brightness, which is generally Rayleigh distributed. The main interest of the technique is its fully automatic character. This is insured by an initial contour that is not set by the user, like in classical snake-based algorithms, but estimated and, thus, adapted to each image. Its estimation combines two pieces of information extracted from the a posteriori probability function of the contour position: the function maximum location (or maximum a posteriori estimator) and the first zero-crossing of its derivative. Then, starting from the initial contour, a region of interest is automatically selected and the process iterated until the contour evolution can be ignored. In vivo coronary images from 15 patients, acquired with the 20-MHz central frequency Jomed Invision ultrasound scanner, were segmented with the developed method. Automatic contours were compared to those manually drawn by two physicians in terms of mean absolute difference. The results demonstrate that the error between automatic contours and the average of manual ones is of small amplitude, and only very slightly higher (0.099 +/- 0.032 mm) than the interexpert error (0.097 +/- 0.027 mm).

Automated three-dimensional assessment of coronary artery anatomy with intravascular ultrasound scanning

BACKGROUND

Angiography allows the definition of advanced, severe stages of coronary artery disease, but early atherosclerotic lesions, which do not lead to luminal stenosis, are not identified reliably. In contrast, intravascular ultrasound scanning allows the precise characterization and quantification of a wide range of atherosclerotic lesions, independent of the severity of luminal stenosis.

METHODS

Three-dimensional (3-D) reconstruction of entire coronary segments is possible with the integration of sequential 2-dimensional tomographic images and allows volumetric analysis of coronary arteries.

RESULTS

Automated systems able to recognize lumen and vessel borders and to display 3-D images are becoming available.

CONCLUSION

These systems have the potential for on-line 3-D image reconstruction for clinical decision-making and fast routine volumetric analysis in research studies. This review describes 3-D intravascular ultrasound scanning acquisition, analysis, and processing, and the associated technical challenges.

A pulsating coronary vessel phantom for two- and three-dimensional intravascular ultrasound studies

The evaluation of new techniques for 2-D and 3-D intravascular ultrasound (US) imaging (IVUS) often requires the use of a pulsating coronary phantom. This study describes the design, construction and evaluation of a phantom simulating the pulsation of a human coronary artery for IVUS studies. Polyvinyl alcohol (PVA) cryogel was used as a tissue mimic for the coronary vessel, which was incorporated in a custom-built assembly. The phantom was programmed to pulsate under servomotor control, to model the pulsation of a normal coronary artery and 2-D IVUS images were obtained using an IVUS imaging catheter. To evaluate the performance of the phantom, the lumen area variation of the phantom was determined and compared with the programmed pulsation waveforms. Our results showed that phantom pulsation correlated well with the programmed pulsation waveform (r = 0.97). The deviation of the least squares line from the line of identity was calculated to be < 4%.

Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound

The precise tomographic assessment of coronary artery disease by intravascular ultrasound (IVUS) is useful in quantitative studies. Such studies require identification of luminal and medial-adventitial (MA) borders in a sequence of IVUS images. We have developed a three-dimensional (3D) active-surface system for border detection that facilitates the analysis of many images with minimal user interaction. To assess the validity of the technique, luminal and MA borders in 529 end-diastolic images from nine coronary arterial segments (58.8 +/- 14.2 images per patient) were traced manually by four experienced observers. The computer-detected borders were compared with borders determined by the four observers using a modified Williams' index (WI), the ratio of inter-observer variability to computer-observer variability. While manual tracing required 49.2 +/- 12.1 min for analysis, the analysis system identified luminal (R2 = 0.92) and MA borders (R2 = 0.97) in 13.8 +/- 4.0 min, a decrease of 35.4 min (p < 0.000001). The computer minus observer differences in lumen area and MA area were -0.88 +/- 0.90 and -0.07 +/- 0.63 mm2. Therefore, the computer system underestimated both lumen and MA area, but this effect was very small in MA area. The WI values and 95% confidence intervals were 0.98 (0.89,1.06) for luminal border detection and 0.99 (0.95,1.04) for MA border detection. Plaque volume measurements, a common endpoint of clinical trials, also verified the accuracy of the technique (R2 = 0.98). The proposed 3D active-surface border detection system provides a faster and less-tedious alternative to manual tracing for assessment of coronary artery anatomy in vivo.

B-spline methods for interactive segmentation and modeling of lumen and vessel surfaces in three-dimensional intravascular ultrasound

Intravascular ultrasound (IVUS) provides direct depiction of coronary anatomy, including the degree and extent of coronary plaque, useful in quantitative research or clinical studies. These studies require fast and accurate analysis of coronary morphometry in volumetric IVUS images. Semi-automated interaction techniques are important for this task due to limitations of automated processing. We present B-spline-based surface fitting and manipulation methods that provide a foundation for such interaction techniques. They can be integrated easily with previously developed segmentation algorithms to provide a semi-automated segmentation system for identification of luminal and vessel borders in volumetric IVUS images.

Cell-based dual snake model: a new approach to extracting highly winding boundaries in the ultrasound images

Two common deficiencies of most conventional deformable models are the need to place the initial contour very close to the desired boundary and the incapability of capturing a highly winding boundary for sonographic boundary extraction. To remedy these two deficiencies, a new deformable model (namely, the cell-based dual snake model) is proposed in this paper. The basic idea is to apply the dual snake model in the cell-based deformation manner. While the dual snake model provides an effective mechanism allowing a distant initial contour, the cell-based deformation makes it possible to catch the winding characteristics of the desired boundary. The performance of the proposed cell-based dual snake model has been evaluated on synthetic images with simulated speckles and on the clinical ultrasound (US) images. The experimental results show that the mean distances from the derived to the desired boundary points are 0.9 +/- 0.42 pixels and 1.29 +/- 0.39 pixels for the synthetic and the clinical US images, respectively.

A dual-snake model of high penetrability for ultrasound image boundary extraction

Most deformable models require the initial contour to be placed close to the boundary of the object of interest for boundary extraction of ultrasound (US) images, which is impractical in many clinical applications. To allow a distant initial contour, a new dual-snake model promising high penetrability through the interference of the noises is proposed in this paper. The proposed dual-snake model features a new far-reaching external force, called the discrete gradient flow, a connected component-weighted image force, and an effective stability evaluation of two underlying snakes. The experimental results show that, with a distant initial contour, the mean distance from the derived boundary to the desired boundary is less than 1.4 pixels, and most snake elements are within 2.7 pixels of the desired boundaries for the synthetic images with CNR > or =1. For the clinical US images, the mean distance is less than 1.9 pixels, and most snake elements are within 3 pixels of the desired boundaries.

Ultrasound imaging in three and four dimensions

Three-dimensional (3D) reconstruction of ultrasound images was first demonstrated nearly 15 years ago, but only now is becoming a clinical reality. In the meantime, methods for 3D reconstruction of CT and MRI images have achieved an advanced state of development, and 3D imaging with these modalities has been applied widely in clinical practice. 3D applications in ultrasound have lagged behind CT and MRI, because ultrasound data is much more difficult to render in 3D, for a variety of technical reasons, than either CT or MRI data. Only in the past few years has the computing power of ultrasound equipment reached a level adequate enough for the complex signal processing tasks needed to render ultrasound data in three dimensions. At this point in time, the clinical application of 3D ultrasound is likely to advance rapidly, as improved 3D rendering technology becomes more widely available. This article is a review of the present status of 3D ultrasound imaging. It begins by comparing the characteristics of CT, MRI, and ultrasound image data that either make these data amenable or not amenable to 3D reconstruction. The article then considers the technical features involved with acquiring an ultrasound 3D data set and the mechanisms for reconstructing the images. Finally, the article reviews the literature that is available regarding clinical application of 3D ultrasound in obstetrics, ultrasound, the abdomen, and blood vessels.