Deformable boundary finding in medical images by integrating gradient and region information

Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.     

Line and boundary detection in speckle images

This paper considers the problem of detecting lines in speckle imagery, such as that produced by synthetic aperture radar (SAR) or ultrasound techniques. Using the physical principles that account for the speckle phenomenon, we derive the optimal detector for lines in fully developed speckle, and we compare the optimal detector to several suboptimal detection rules that are more computationally efficient. We show that when the noise is uncorrelated, a very simple suboptimal detection rule is nearly optimal, and that even in colored speckle, a related class of detectors can approach optimal performance. Finally, we also discuss the application of this technique to medical ultrasonic images, where the detection of tissue boundaries is considered as a problem of line detection.     

Contour detection of the left ventricular cavity from angiographic images

A knowledge-based heuristic method is proposed to outline the left ventricular cavity from end-diastolic angiographic images in right anterior oblique projection in man. The algorithm search for the optimal path in a weighted graph, representing the state space, can implicitly be defined as the set of all the contour segments that can be produced by successor operators. Applications to routine images are presented.     

A methodology for evaluation of boundary detection algorithms onmedical images

Image segmentation is the partition of an image into a set of nonoverlapping regions whose union is the entire image. The image is decomposed into meaningful parts which are uniform with respect to certain characteristics, such as gray level or texture. In this paper, we propose a methodology for evaluating medical image segmentation algorithms wherein the only information available is boundaries outlined by multiple expert observers. In this case, the results of the segmentation algorithm can be evaluated against the multiple observers' outlines. We have derived statistics to enable us to find whether the computer-generated boundaries agree with the observers' hand-outlined boundaries as much as the different observers agree with each other. We illustrate the use of this methodology by evaluating image segmentation algorithms on two different applications in ultrasound imaging. In the first application, we attempt to find the epicardial and endocardial boundaries from cardiac ultrasound images, and in the second application, our goal is to find the fetal skull and abdomen boundaries from prenatal ultrasound images     

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

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. In this paper, we 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. We conclude that the automated procedure can replace the manual procedure and leads to an improved performance.     

Bayesian estimation of ventricular contours in angiographic images

A method for left ventricular contour determination in digital angiographic images is presented. The problem is formulated in a Bayesian framework, adopting as the estimation criterion the maximum a posterior probability (MAP). The true contour is modeled as a one-dimensional noncausal Gauss-Markov random field and the observed image is described as the superposition of an ideal image (deterministic function of the real contour) with white Gaussian noise. The proposed algorithm estimates simultaneously the contour and the model parameters by implementing an adaptive version of the iterated conditional modes algorithm. The convergence of this scheme is proved and its performance evaluated on both synthetic and real angiographic images. The method exhibits robustness against image artifacts and the contours obtained are considered good by expert clinicians. Being completely data-driven and fast, the proposed algorithm is suitable for routine clinical use.     

Wavelet-based lossless compression of coronary angiographic images

The final diagnosis in coronary angiography has to be performed on a large set of original images. Therefore, lossless compression schemes play a key role in medical database management and telediagnosis applications. This paper proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new way of coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of 20 angiograms show that the algorithm outperforms the embedded zerotree coder, combined with the integer wavelet transform, by 0.38 bpp, the set partitioning coder by 0.21 bpp, and the lossless JPEG coder by 0.71 bpp. The scheme is a good candidate for radiological applications such as teleradiology and picture archiving and communications systems (PACS's).     

Watershed-presegmented snake for boundary detection and tracking of left ventricle in echocardiographic images.

In this paper, an automated method of boundary detection of the left ventricle (LV) is proposed. The method uses a watershed transform and morphological operation to locate the region containing the LV, then performs snake deformation with a multiscale directional edge map for the detection of the endocardial boundary of the LV.     

Automatic ventricular cavity boundary detection from sequential ultrasound images using simulated annealing

An automatic algorithm has been developed for high-speed detection of cavity boundaries in sequential 2-D echocardiograms using an optimization algorithm called simulated annealing (SA). The algorithm has three stages. (1) A predetermined window of size nxm is decimated to size n'xm' after low-pass filtering. (2) An iterative radial gradient algorithm is employed to determine the center of gravity (CG) of the cavity. (3) 64 radii which originate from the CG defined in stage 2 are bounded by the high-probability region. Each bounded radius is defined as a link in a 1-D, 64-member cyclic Markov random field. This algorithm is unique in that it compounds spatial and temporal information along with a physical model in its decision rule, whereas most other algorithms base their decisions on spatial data alone. This is the first implementation of a relaxation algorithm for edge detection in echocardiograms. Results attained using this algorithm on real data have been highly encouraging.     

Comments on: A methodology for evaluation of boundary detection algorithms on medical images

In this paper we analyze a result previously published about a comparison between two statistical tests used for evaluation of boundary detection algorithms on medical images. We conclude that the statement made by Chalana and Kim (1997) about the performance of the percentage test has a weak theoretical foundation, and according to our results, is not correct. In addition, we propose a one-sided hypothesis test for which the acceptance region can be determined in advance, as opposed to the two-sided confidence intervals proposed in the original paper, which change according to the estimated quantity.     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

Model-based quantitation of 3-D magnetic resonance angiographic images

Quantification of the degree of stenosis or vessel dimensions are important for diagnosis of vascular diseases and planning vascular interventions. Although diagnosis from three-dimensional (3-D) magnetic resonance angiograms (MRA's) is mainly performed on two-dimensional (2-D) maximum intensity projections, automated quantification of vascular segments directly from the 3-D dataset is desirable to provide accurate and objective measurements of the 3-D anatomy. A model-based method for quantitative 3-D MRA is proposed. Linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. A novel image feature to guide the deformation of the central vessel axis is introduced. Subsequently, concepts of deformable models are combined with knowledge of the physics of the acquisition technique to accurately segment the vessel wall and compute the vessel diameter and other geometrical properties. The method is illustrated and validated on a carotid bifurcation phantom, with ground truth and medical experts as comparisons. Also, results on 3-D time-of-flight (TOF) MRA images of the carotids are shown. The approach is a promising technique to assess several geometrical vascular parameters directly on the source 3-D images, providing an objective mechanism for stenosis grading.     

Deformable model of the human iris for measuring ocular torsionfrom video images

The authors describe a deformable model of the human iris, which forms part of a system for accurate offline measurement of binocular eye movements, particularly cyclotorsion (torsion), from video image sequences. At least two existing systems measure torsion from infrared video images by pupil tracking followed by cross-correlation of bandpass filtered iris sectors. Unfortunately, pupil expansion and contraction reduce the accuracy of this method. In addition, infrared iris images typically contain very little texture, so correlation can be unreliable. A five-parameter deformable model was therefore developed for taken in visible light. This model can translate (horizontal and vertical eye motion), rotate (torsion) and scale both uniformly and radially (pupil changes). A series of software simulations and hardware tests suggest that torsion measurements obtained with the model are repeatable and accurate to within 0.1°. This performance is illustrated by analysing binocular torsion during fixation on a static target; the results match previously published data from other equipment     

A switching median filter with boundary discriminative noise detection for extremely corrupted images.

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups--lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy--in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.     

Edge-guided boundary delineation in prostate ultrasound images

Accurate detection of prostate boundaries is required in many diagnostic and treatment procedures for prostate disease. In this paper, a new paradigm for guided edge delineation is described, 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, we 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.     

Automated boundary delineation in infrared ocean images

A mathematical morphology technique aids in interpreting satellite infrared images of the Gulf Stream region. The method delineates the North Wall clearly, with less smoothing than human analysts. Performance in locating warm-core eddies is comparable to that of humans and another automated procedure. A variation of the method finds cold-core eddies     

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

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.     

Improved approach to boundary location in two-dimensionalechocardiographic images

The widespread utilisation of digital two-dimensional echocardiography has led to an increasing interest in the possibility of automated analysis of the images using this technique. For quantitative analysis of cardiac function the detection of the endocardium and epicardium is of considerable importance yet, particularly in certain key views, most conventional edge finding algorithms can be easily undermined because of the poor quality of the images and the appearance of irrelevant structures within the viewing area. The authors describe a new approach to boundary detection which, while based principally on a radial search technique, extends the conventional analysis through the addition of further contour analysis in two preprocessed images. The algorithms are described in detail and results are presented to illustrate the advantageous features of the approach proposed     

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

This paper presents a novel deformable model for automatic segmentation of prostates from three-dimensional ultrasound images, by statistical matching of both shape and texture. A set of Gabor-support vector machines (G-SVMs) are positioned on different patches of the model surface, and trained to adaptively capture texture priors of ultrasound images for differentiation of prostate and nonprostate tissues in different zones around prostate boundary. Each G-SVM consists of a Gabor filter bank for extraction of rotation-invariant texture features and a kernel support vector machine for robust differentiation of textures. In the deformable segmentation procedure, these pretrained G-SVMs are used to tentatively label voxels around the surface of deformable model as prostate or nonprostate tissues by a statistical texture matching. Subsequently, the surface of deformable model is driven to the boundary between the tentatively labeled prostate and non-prostate tissues. Since the step of tissue labeling and the step of label-based surface deformation are dependent on each other, these two steps are repeated until they converge. Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.     

Optimal edge detection in two-dimensional images

This paper presents a new edge detection scheme that detects two-dimensional (2-D) edges by a curve-segment-based detection functional guided by the zero-crossing contours of the Laplacian-of-Gaussian (LOG) to approach the true edge locations. The detection functional is shown to be optimal in terms of signal-to-noise ratio (SNR) and edge localization accuracy; it also preserves the nice scaling property held uniquely by the LOG in scale space.