Contour extraction from cardiac MRI studies using snakes

The author investigated automatic extraction of left ventricular contours from cardiac magnetic resonance imaging (MRI) studies. The contour extraction algorithms were based on active contour models, or snakes. Based on cardiac MR image characteristics, the author suggested algorithms for extracting contours from these large data sets. The author specifically considered contour propagation methods to make the contours reliable enough despite noise, artifacts, and poor temporal resolution. The emphasis was on reliable contour extraction with a minimum of user interaction. Both spin echo and gradient echo studies were considered. The extracted contours were used for determining quantitative measures for the heart and could also be used for obtaining graphically rendered cardiac surfaces.     

Sequential cardiac segmentation by seed contour tracking

A sequential cardiac segmentation method based on an active contour model, initial seed contour tracking (SCT) scheme, and phase contrast magnetic resonance imaging (MRI) has been developed to improve the accuracy of automatic sequential segmentation of the myocardial boundaries, especially the endocardial boundary. The performance of the proposed method was assessed by experiments performed on phase contrast MRI data sets from three normal human volunteers. Experimental results showed that the propagation of errors caused by improper positioning of initial seed contours in sequential cardiac segmentation was reduced significantly by the use of the SCT scheme.     

Semiautomated segmentation of myocardial contours for fast strain analysis in cine displacement-encoded MRI

The purposes of this study were to develop a semiautomated cardiac contour segmentation method for use with cine displacement-encoded MRI and evaluate its accuracy against manual segmentation. This segmentation model was designed with two distinct phases: preparation and evolution. During the model preparation phase, after manual image cropping and then image intensity standardization, the myocardium is separated from the background based on the difference in their intensity distributions, and the endo- and epi-cardial contours are initialized automatically as zeros of an underlying level set function. During the model evolution phase, the model deformation is driven by the minimization of an energy function consisting of five terms: model intensity, edge attraction, shape prior, contours interaction, and contour smoothness. The energy function is minimized iteratively by adaptively weighting the five terms in the energy function using an annealing algorithm. The validation experiments were performed on a pool of cine data sets of five volunteers. The difference between the semiautomated segmentation and manual segmentation was sufficiently small as to be considered clinically irrelevant. This relatively accurate semiautomated segmentation method can be used to significantly increase the throughput of strain analysis of cine displacement-encoded MR images for clinical applications.      

Cardiac image segmentation for contrast agent videodensitometry

Indicator dilution techniques are widely used in the intensive care unit and operating room for cardiac parameter measurements. However, the invasiveness of current techniques represents a limitation for their clinical use. The development of stable ultrasound contrast agents allows new applications of the indicator dilution method. Ultrasound contrast agent dilutions permit an echographic noninvasive measurement of cardiac output, ejection fraction, and blood volumes. The indicator dilution curves are measured by videodensitometry of specific regions of interest and processed for the cardiac parameter assessment. Therefore, the major indicator dilution imaging issue is the detection of proper contrast videodensitometry regions that maximize the signal-to-noise ratio of the measured indicator dilution curves. This paper presents an automatic contour detection algorithm for indicator dilution videodensitometry. The algorithm consists of a radial filter combined with an outlier correction. It maximizes the region of interest by excluding cardiac structures that act as interference to the videodensitometric analysis. It is fast, projection independent, and allows the simultaneous detection of multiple contours in real time. The system is compared to manual contour definition on both echographic and magnetic resonance images.     

Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features

Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties.     

Automated Motion Estimation for 2-D Cine DENSE MRI

Cine displacement encoding with stimulated echoes (DENSE) is a magnetic resonance (MR) method that directly encodes tissue displacement into MR phase images. This technique has successfully interrogated many forms of tissue motion, but is most commonly used to evaluate cardiac mechanics. Currently, motion analysis from cine DENSE images requires manually delineated anatomical structures. An automated analysis would improve measurement throughput, simplify data interpretation, and potentially access important physiological information during the MR exam. In this paper, we present the first fully automated solution for the estimation of tissue motion and strain from 2-D cine DENSE data. Results using both simulated and human cardiac cine DENSE data indicate good agreement between the automated algorithm and the standard semi-manual analysis method.     

Accurate automatic analysis of cardiac cine images

Acquisition of noncontrast agent cine cardiac magnetic resonance (CMR) gated images through the cardiac cycle is, at present, a well-established part of examining cardiac global function. However, regional quantification is less well established. We propose a new automated framework for analyzing the wall thickness and thickening function on these images that consists of three main steps. First, inner and outer wall borders are segmented from their surrounding tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for Markov-Gibbs shape and appearance models of the object-of-interest and its background. In the second step, point-to-point correspondences between the inner and outer borders are found by solving the Laplace equation and provide initial estimates of the local wall thickness and the thickening function index. Finally, the effects of the segmentation error is reduced and a continuity analysis of the LV wall thickening is performed through iterative energy minimization using a generalized Gauss-Markov random field (GGMRF) image model. The framework was evaluated on 26 datasets from clinical cine CMR images that have been collected from patients with eleven independent studies, with chronic ischemic heart disease and heart damage. The performance evaluation of the proposed segmentation approach, based on the receiver operating characteristic (ROC) and Dice similarity coefficients (DSC) between manually drawn and automatically segmented contours, confirmed a high robustness and accuracy of the proposed segmentation approach. Furthermore, the Bland-Altman plot is used to assess the limit of agreement of our measurements of the global function parameters compared to the ground truth. Importantly, comparative results on the publicly available database (MICCAI 2009 Cardiac MR Left Ventricle Segmentation) demonstrated a superior performance of the proposed segmentation approach over published methods.     

Left Ventricle Segmentation Using Model Fitting and Active Surfaces

A method to perform 4D (3D over time) seg mentation of the left ventricle of a mouse heart using a set of B mode cine slices acquired in vivo from a series of short axis scans is described. We incorporate previ ously suggested methods such as temporal propagation, the gradient vector flow active surface, superquadric models, etc. into our proposed 4D segmentation of the left ventricle. The contributions of this paper are incor poration of a novel despeckling method and the use of locally fitted superellipsoid models to provide a better initialization for the active surface segmentation algorithm. Average distances of the improved surface segmentation to a manually segmented surface through out the entire cardiac cycle and cross-sectional contours are provided to demonstrate the improvements pro duced by the proposed 4D segmentation.     

Constrained detection of left ventricular boundaries from cine CT images of human hearts

Detection of the left ventricular (LV) endocardial (inner) and epicardial (outer) boundaries in cardiac images, provided by fast computer tomography (cine CT), magnetic resonance (MR), or ultrasound (echocardiography), is addressed. The automatic detection of the LV boundaries is difficult due to background noise, poor contrast, and often unclear differentiation of the tissue characteristics of the ventricles, papillary muscles, and surrounding tissues. An approach to the automatic ventricular boundary detection that employs set-theoretic techniques, and is based on incorporating a priori knowledge of the heart geometry, its brightness, spatial structure, and temporal dynamics into the boundaries detection algorithm is presented. Available knowledge is interpreted as constraint sets in the functional space, and the consistent boundaries are considered to belong to the intersection of all the introduced sets, thus satisfying the a priori information. An algorithm is also suggested for the simultaneous detection of the endocardial and epicardial boundaries of the LV. The procedure is demonstrated using cine CT images of the human heart.     

A neural network-based stochastic active contour model (NNS-SNAKE)for contour finding of distinct features

Contour finding of distinct features in 2-D/3-D images is essential for image analysis and computer vision. To overcome the potential problems associated with existing contour finding algorithms, we propose a framework, called the neural network-based stochastic active contour model (NNS-SNAKE), which integrates a neural network classifier for systematic knowledge building, an active contour model (also known as the "Snake") for automated contour finding using energy functions, and the Gibbs sampler to help the snake to find the most probable contour using a stochastic decision mechanism. Successful application of the NNS-SNAKE to extraction of several types of contours on magnetic resonance (MR) images is presented.     

ROPES: a semiautomated segmentation method for accelerated analysis of three-dimensional echocardiographic data

Echocardiography (cardiac ultrasound) is today the predominant technique for quantitative assessment of cardiac function and valvular heart lesions. Segmentation of cardiac structures is required to determine many important diagnostic parameters. As the heart is a moving organ, reliable information can be obtained only from three-dimensional (3-D) data over time (3-D + time = 4-D). Due to their size, the resulting four-dimensional (4-D) data sets are not reasonably accessible to simple manual segmentation methods. Automatic segmentation often yields unsatisfactory results in a clinical environment, especially for ultrasonic images. We describe a semiautomated segmentation algorithm (ROPES) that is able to greatly reduce the time necessary for user interaction and its application to extract various parameters from 4-D echocardiographic data. After searching for candidate contour points, which have to fulfill a multiscale edge criterion, the candidates are connected by minimizing a cost function to line segments that then are connected to form a closed contour. The contour is automatically checked for plausibility. If necessary, two correction methods that can also be used interactively are applied (fitting of other line segments into the contour and searching for additional candidates with a relaxed criterion). The method is validated using in vivo transesophageal echocardiographic data sets.     

Tag and contour detection in tagged MR images of the left ventricle

Tracking magnetic resonance tags in myocardial tissue promises to be an effective tool for the assessment of myocardial motion. The authors describe a hierarchy of image processing steps which rapidly detects both the contours of the myocardial boundaries of the left ventricle and the tags within the myocardium. The method works on both short axis and long axis images containing radial and parallel tag patterns, respectively. Left ventricular boundaries are detected by first removing the tags using morphological closing and then selecting candidate edge points. The best inner and outer boundaries are found using a dynamic program that minimizes a nonlinear combination of several local cost functions. Tags are tracked by matching a template of their expected profile using a least squares estimate. Since blood pooling, contiguous and adjacent tissue, and motion artifacts sometimes cause detection errors, a graphical user interface was developed to allow user correction of anomalous points. The authors present results on several tagged images of a human. A fully automated run generally finds the endocardial boundary and the tag lines extremely well, requiring very little manual correction. The epicardial boundary sometimes requires more intervention to obtain an acceptable result. These methods are currently being used in the analysis of cardiac strain and as a basis for the analysis of alternate tag geometries.     

Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation

Inhaled hyperpolarized helium-3 (3He) gas is a new magnetic resonance (MR) contrast agent that is being used to study lung functionality. To evaluate the total lung ventilation from the hyperpolarized 3He MR images, it is necessary to segment the lung cavities. This is difficult to accomplish using only the hyperpolarized 3He MR images, so traditional proton (1H) MR images are frequently obtained concurrent with the hyperpolarized 3He MR examination. Segmentation of the lung cavities from traditional proton (1H) MRI is a necessary first step in the analysis of hyperpolarized 3He MR images. In this paper, we develop an active contour model that provides a smooth boundary and accurately captures the high curvature features of the lung cavities from the 1H MR images. This segmentation method is the first parametric active contour model that facilitates straightforward merging of multiple contours. The proposed method of merging computes an external force field that is based on the solution of partial differential equations with boundary condition defined by the initial positions of the evolving contours. A theoretical connection with fluid flow in porous media and the proposed force field is established. Then by using the properties of fluid flow we prove that the proposed method indeed achieves merging and the contours stop at the object boundary as well. Experimental results involving merging in synthetic images are provided. The segmentation technique has been employed in lung 1H MR imaging for segmenting the total lung air space. This technology plays a key role in computing the functional air space from MR images that use hyperpolarized 3He gas as a contrast agent.     

Automatic contour definition on left ventriculograms by image evidence and a multiple template-based model

An algorithm which utilizes digital image processing and pattern recognition methods for automated definition of left ventricular (LV) contours is presented. Digital image processing and pattern recognition techniques are applied to digitally acquired radiographic images of the heart to extract the LV contours required for quantitative analysis of cardiac function. Knowledge of the image domain is invoked at each step of the algorithm to orient the data search and thereby the complexity of the solution. A knowledge-based image transformation, directional gradient search, expectations of object versus background location, least-cost path searches by dynamic programming, and a digital representation of possible versus impossible ventricular shape are exploited. The digital representation, composed of a set of characteristic templates, was created using contours obtained by manual tracing. The algorithm was tested by application of three sets of 25 images each. Test set one and two were used as training sets for creation of the model for contour correction. Model-based correction proved to be an effective technique, producing significant reduction of error in the final contours.     

Multiscale Probabilistic Dithering for Suppressing Contour Artifacts in Digital Images

A method is proposed for reducing the visibility of “contour artifacts,” i.e., false contours resulting from color quantization in digital images. The method performs a multiscale analysis on the neighborhood of each pixel, determines the presence and scale of contour artifacts, and probabilistically dithers (perturbs) the color of the pixel. The overall effect is to “break down” the false contours, making them less visible. The proposed method may be used to reduce contour artifacts at the same bit depth as the input image or at higher bit depths. The contour artifact detection mechanism ensures that artifact-free regions remain unaffected during the process.      

Feature-directed spiral coding of magnetic resonance images

The authors investigate the encoding of magnetic resonance (MR) images of the human body using various lossless techniques, and presents a new form of spiral encoding. The algorithm used relies partially on the overall shape of the bounding contour of the image in achieving the compression and uses a traditional run-based technique combined with an adaptive Huffman coder to encode the complete image. Comparisons are made between the feature-directed spiral encoding and the traditional paths; the latter include the scanning pattern associated with the normal raster scanned display and the path for a display that could be used in following a linearised quadtree encoding. The new method tracks the `greater' contour of the overall image and, once the path has been established and tuples recorded, the inner contours are automatically generated. The process is repeated for each of the inner contours with a reducing radius towards the centre. The results are given for the various techniques in terms of compression ratios. The new spiralling method achieves an approximate 5.29% saving over the traditional techniques and also gives structure to the compressed image     

Design and evaluation of an automatic procedure for detection of large misregistration of medical images

In many cases the combined assessment of three-dimensional anatomical and functional images [single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT)] is necessary to determine the precise nature and extent of lesions. It is important, prior to performing the addition, subtraction, or any other combination of the images, that they be adequately aligned and registered either by experienced radiologists via visual inspection, mental reorientation and overlap of slices, or by an automated registration algorithm. To be useful clinically, the latter case requires validation. The human capacity to evaluate registration results visually is limited and time consuming. This paper describes an algorithmic procedure to provide proxy measures for human assessment that discriminate between badly misregistered pairs of brain images and those likely to be clinically useful. The new algorithm consists of four major steps: segmentation of brain and skin/air boundaries, contour extraction, computation of the principal axes, and computation of the registration quality measures from the contour volumes. The test data were MR and CT brain images. The results of the present study indicate that the use of a measure based on the combination of brain and skin contours and a principal axis function is a good first step to reduce the number of badly registered images reaching the clinician.     

Integrating local distribution information with level set for boundary extraction

This paper presents a general object boundary extraction model for piecewise smooth images, which incorporates local intensity distribution information into an edge-based implicit active contour. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different region-based operators: a Gaussian mixture model (GMM)-based intensity distribution estimator and the Hueckel operator. We propose the local distribution fitting model for more accurate segmentation, which incorporates the operator outcomes into the recent local binary fitting (LBF) model. The GMM and the Hueckel model parameters are estimated before contour evolution, which enables the use of the proposed model without the need for initial contour selection, i.e., the level set function is initialized with a random constant instead of a distance map. Thus our model essentially alleviates the initialization sensitivity problem of most active contours. Experiments on synthetic and real images show the improved performance of our approach over the LBF model.     

Retrospective correction of MR intensity inhomogeneity byinformation minimization

In this paper, the problem of retrospective correction of intensity inhomogeneity in magnetic resonance (MR) images is addressed. A novel model-based correction method is proposed, based on the assumption that an image corrupted by intensity inhomogeneity contains more information than the corresponding uncorrupted image. The image degradation process is described by a linear model, consisting of a multiplicative and an additive component which are modeled by a combination of smoothly varying basis functions. The degraded image is corrected by the inverse of the image degradation model. The parameters of this model are optimized such that the information of the corrected image is minimized while the global intensity statistic is preserved. The method was quantitatively evaluated and compared to other methods on a number of simulated and real MR images and proved to be effective, reliable, and computationally attractive. The method can be widely applied to different types of MR images because it solely uses the information that is naturally present in an image, without making assumptions on its spatial and intensity distribution. Besides, the method requires no preprocessing, parameter setting, nor user interaction. Consequently, the proposed method may be a valuable tool in MR image analysis.     

A Fully Global Approach to Image Segmentation via Coupled Curve Evolution Equations

In this paper, we develop a novel region-based approach to snakes designed to optimally separate the values of certain image statistics over a known number of region types. Multiple sets of contours deform according to a coupled set of curve evolution equations derived from a single global cost functional. The resulting active contour model, in contrast to many other edge and region based models, is fully global in that the evolution of each curve depends at all times upon every pixel in the image and is directly coupled to the evolution of every other curve regardless of their mutual proximity. As such evolving contours enjoy a very wide “field of view,” endowing the algorithm with a robustness to initial contour placement above and beyond the significant improvement exhibited by other region based snakes over earlier edge based snakes.