Biomedical subjects

Scott T Acton

Publications and source records attributed to Scott T Acton.

18 recordsLinked to original sources

PubMed2007

Quantitative and topographical evaluation of ankle articular cartilage using high resolution MRI.

The objectives of this study were to quantitatively evaluate the articular cartilage layers of the ankle and describe the cartilage topographical distribution across the joint surfaces using high resolution MRI and image segmentation. An anisotropic diffusion noise reduction algorithm and a directional gradient vector flow (dGVF) snake segmentation algorithm were applied to cartilage sensitive MR images. Eight cadaveric ankles were studied. Six repeated data sets were acquired in five of the ankles. Quantitative parameters were calculated for each cartilage layer; coefficients of variation (CV) were calculated from the six repeated data sets; and 3D thickness distribution maps were generated. The noise reduction algorithm produced marked image enhancement. Mean cartilage thickness ranged from 0.91 +/- 0.08 mm in the fibula to 1.34 +/- 0.14 mm in the talus. Mean cartilage volume was 3.32 +/- 0.55 ml, 1.72 +/- 0.25 ml, and 0.35 +/- 0.06 ml for the talus, tibia, and fibula, respectively. Mean CV ranged 2.82%-5.04% for quantitative parameters in the talus and tibia. The reported noise reduction and segmentation technique allow precise extraction of ankle cartilage and 3D reconstructions show that the thickest cartilage occurs over the talar shoulders, where osteochondritits dissecans (OCD) lesions commonly occur.

Algorithms↗

PubMed2006

A Monte Carlo approach to rolling leukocyte tracking in vivo.

Tracking the movement of rolling leukocytes in vivo contributes to the understanding of the mechanism of the inflammatory process and to the development of anti-inflammatory drugs. Several roadblocks exist that hinder successful automated tracking including the moving background, the severe image noise and clutter, the occlusion of the target leukocyte by other leukocytes and structures, the jitter caused by the breathing movement of the living animal, and the weak image contrast. In this paper, a Monte Carlo tracker is developed for automatically tracking a single rolling leukocyte in vivo. Based on the leukocyte movement information and the image intensity features, a specialized sample-weighting criterion is tailored to the application. In comparison with a snake-based tracker, our experiments show that, as the noise intensity level increases, the performance of the snake tracker degrades more than that of the Monte Carlo tracker. In cases, where the leukocyte is observed in contact with the vessel wall, the Monte Carlo tracker is less affected by the image clutter. From tracking within 99 intravital microscopic video sequences, the Monte Carlo tracker exhibits superior performance in the reduced localization error and the increased number of frames tracked when compared with the centroid tracker, the correlation tracker and the GVF snake tracker.

Algorithms↗

PubMed2006

Analysis of leukocyte rolling in vivo and in vitro.

Leukocyte rolling is an important step for the successful recruitment of leukocytes from blood to tissues mediated by a specialized group of glycoproteins termed selectins. Because of the dynamic process of leukocyte rolling, binding of selectins to their respective counter-receptors (selectin ligands) needs to fulfill three major requirements: (1) rapid bond formation, (2) high tensile strength, and (3) fast dissociation rates. These criteria are perfectly met by selectins, which interact with specific carbohydrate determinants on selectin ligands. This chapter describes the theoretical background, technical requirements, and analytical tools needed to quantitatively assess leukocyte rolling in vivo and in vitro. For the in vivo setting, intravital microscopy allows the observation and recording of leukocyte rolling under different physiological and pathological conditions in almost every organ. Real-time and off-line analysis tools help to assess geometric, hemodynamic, and rolling parameters. Under in vitro conditions, flow chamber assays such as parallel plate flow chamber systems have been the mainstay to study interactions between leukocytes and adhesion molecules under flow. In this setting, adhesion molecules are immobilized on plastic, in a lipid monolayer, or presented on cultured endothelial cells on the chamber surface. Microflow chambers are available for studying leukocyte adhesion in the context of whole blood and without blood cell isolation. The microscopic observation of leukocyte rolling in different in vivo and in vitro settings has significantly contributed to our understanding of the molecular mechanisms responsible for the stepwise extravasation of leukocytes into inflamed tissues.

Cell Adhesion↗

PubMed2006

Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes.

The accuracy of the surface extraction of magnetic resonance images of highly congruent joints with thin articular cartilage layers has a significant effect on the percentage errors and reproducibility of quantitative measurements (e.g., thickness and volume) of the articular cartilage. Traditional techniques such as gradient-based edge detection are not suitable for the extraction of these surfaces. This paper studies the extraction of articular cartilage surfaces using snakes, and a gradient vector flow (GVF)-based external force is proposed for this application. In order to make the GVF snake more stable and converge to the correct surfaces, directional gradient is used to produce the gradient vector flow. Experimental results show that the directional GVF snake is more robust than the traditional GVF snake for this application. Based on the newly developed snake model, an articular cartilage surface extraction algorithm is developed. Thickness is computed based on the surfaces extracted using the proposed algorithm. In order to make the thickness measurement more reproducible, a new thickness computation approach, which is called T-norm, is introduced. Experimental results show that the thickness measurement obtained by the new thickness computation approach has better reproducibility than that obtained by the existing thickness computation approaches.

Algorithms↗

PubMed2005

Image stabilization and registration for tracking cells in the microvasculature.

We propose a registration system to be used for tracking cells in intravital video microscopy that 1) stabilizes jitter-the undesired translational displacement of frames due to respiratory movement, etc., and 2) registers frames in a moving field of view (FOV) to allow for cell tracking over an extended range. For the first time, tracking of rolling leukocytes in vivo over a moving FOV is demonstrated. In a fixed FOV, stable background regions are located using a morphological approach. Template subregions are then selected from the stable regions and matched to corresponding locations in a reference frame. We show the effectiveness of the stabilization algorithm by using an active contour to track 15 leukocytes previously untrackable due to jitter. For 30 fixed FOV sequences containing rolling leukocytes, the resulting root-mean-square error (RMSE) is less than 0.5 microm. To align frames in a moving FOV, we present a modified correlation approach to estimate the common region between two consecutive fixed FOVs. We correlate the overlapping regions of the initial frame of the current fixed FOV and the final frame of the previous fixed FOV to register the images in the adjoining moving FOV. The RMSE of our moving FOV registration technique was less than 0.6 mmicrom. In 10 sequences from different venules, we were able to track 11 cells using an active contour approach over moving FOVs.

Algorithms↗

PubMed2005

Data acceptance for automated leukocyte tracking through segmentation of spatiotemporal images.

A crucial task in inflammation research and inflammatory drug validation is leukocyte velocity data collection from microscopic video imagery. Since manual methods are bias-prone and extremely time consuming, automated tracking methods are required to compute cell velocities. However, an automated tracking method is of little practical use unless it is accompanied by a mechanism to validate the tracker output. In this paper, we propose a validation technique that accepts or rejects the output of automated tracking methods. The proposed method first generates a spatiotemporal image from the cell locations given by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte. For segmenting the spatiotemporal images, we employ an edge-direction sensitive nonlinear filter followed by an active contour based technique. The proposed nonlinear filter, the maximum absolute average directional derivative (MAADD), first computes the magnitude of the mean directional derivative over an oriented line segment and then chooses the maximum of all such values within a range of orientations of the line segment. The proposed active contour segmentation is obtained via growing contours controlled by a two-dimensional force field, which is constructed by imposing a Dirichlet boundary condition on the gradient vector flow (GVF) field equations. The performance of the proposed validation method is reported here for the outputs of three different tracking techniques: the method was successful in 97% of the trials using manual tracking, in 94% using correlation tracking and in 93% using active contour tracking.

Algorithms↗

PubMed2005

Inclusion filters: a class of self-dual connected operators.

In this paper, we define a connected operator that either fills or retains the holes of the connected sets depending on application-specific criteria that are increasing in the set theoretic sense. We refer to this class of connected operators as inclusion filters, which are shown to be increasing, idempotent, and self dual (gray-level inversion invariance). We demonstrate self duality for 8-adjacency on a discrete Cartesian grid. Inclusion filters are defined first for binary-valued images, and then the definition is extended to grayscale imagery. It is also shown that inclusion filters are levelings, a larger class of connected operators. Several important applications of inclusion filters are demonstrated-automatic segmentation of the lung cavities from magnetic resonance imagery, user interactive shape delineation in content-based image retrieval, registration of intravital microscopic video sequences, and detection and tracking of cells from these sequences. The numerical performance measures on 100-cell tracking experiments show that the use of inclusion filter improves the total number of frames successfully tracked by five times and provides a threefold reduction in the overall position error.

Algorithms↗

PubMed2005

Intravital leukocyte detection using the gradient inverse coefficient of variation.

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.

Algorithms↗

PubMed2004

Speckle reducing anisotropic diffusion for 3D ultrasound images.

This paper presents an approach for reducing speckle in three dimensional (3D) ultrasound images. A 2D speckle reduction technique, speckle reducing anisotropic diffusion (SRAD), is explored and extended to 3D. 3D SRAD is advantageous in that, like 2D SRAD, it keeps the advantages of the conventional anisotropic diffusion and the traditional speckle reducing filter, the Lee filter, by exploiting the instantaneous coefficient of variation (ICOV). Besides, 3D SRAD uses 3D information; thus it overcomes the shortcoming of the 2D technique that only uses 2D information. The algorithm of 3D SRAD is presented in the continuous domain as well as in the discrete domain. Experiments have been performed on both synthetic and real 3D ultrasound images and the experimental results were compared with those obtained by 3D anisotropic diffusion and the 3D Lee filter. The experimental results show that the quality of the 3D SRAD for speckle reduction in 3D ultrasound images improves upon that of 3D anisotropic diffusion and 3D Lee filter in terms of edge preservation and the smoothness of homogenous regions.

Algorithms↗

PubMed2004

Vessel boundary tracking for intravital microscopy via multiscale gradient vector flow snakes.

Due to movement of the specimen, vasodilation, and intense clutter, the intravital location of a vessel boundary from video microscopy is a difficult but necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. This paper details an active contour model for vessel boundary detection and tracking. In developing the method, two innovations are introduced. First, the B-spline model is combined with the gradient vector flow (GVF) external force. Second, a multiscale gradient vector flow (MSGVF) is employed to elude clutter and to reliably localize the vessel boundaries. Using synthetic experiments and video microscopy obtained via transillumination of the mouse cremaster muscle, we demonstrate that the MSGVF approach is superior to the fixed-scale GVF approach in terms of boundary localization. In each experiment, the fixed scale approach yielded at least a 50% increase in root mean squared error over the multiscale approach. In addition to delineating the vessel boundary so that cells can be detected and tracked, we demonstrate the boundary location technique enables automatic blood flow velocity computation in vivo.

Algorithms↗

PubMed2004

Level set analysis for leukocyte detection and tracking.

We propose a cell detection and tracking solution using image-level sets computed via threshold decomposition. In contrast to existing methods where manual initialization is required to track individual cells, the proposed approach can automatically identify and track multiple cells by exploiting the shape and intensity characteristics of the cells. The capture of the cell boundary is considered as an evolution of a closed curve that maximizes image gradient along the curve enclosing a homogeneous region. An energy functional dependent upon the gradient magnitude along the cell boundary, the region homogeneity within the cell boundary and the spatial overlap of the detected cells is minimized using a variational approach. For tracking between frames, this energy functional is modified considering the spatial and shape consistency of a cell as it moves in the video sequence. The integrated energy functional complements shape-based segmentation with a spatial consistency based tracking technique. We demonstrate that an acceptable, expedient solution of the energy functional is possible through a search of the image-level lines: boundaries of connected components within the level sets obtained by threshold decomposition. The level set analysis can also capture multiple cells in a single frame rather than iteratively computing a single active contour for each individual cell. Results of cell detection using the energy functional approach and the level set approach are presented along with the associated processing time. Results of successful tracking of rolling leukocytes from a number of digital video sequences are reported and compared with the results from a correlation tracking scheme.

Algorithms↗

PubMed2004

Constraining active contour evolution via lie groups of transformation.

We present a novel approach to constraining the evolution of active contours used in image analysis. The proposed approach constrains the final curve obtained at convergence of curve evolution to be related to the initial curve from which evolution begins through an element of a desired Lie group of plane transformations. Constraining curve evolution in such a way is important in numerous tracking applications where the contour being tracked in a certain frame is known to be related to the contour in the previous frame through a geometric transformation such as translation, rotation, or affine transformation, for example. It is also of importance in segmentation applications where the region to be segmented is known up to a geometric transformation. Our approach is based on suitably modifying the Euler-Lagrange descent equations by using the correspondence between Lie groups of plane actions and their Lie algebras of infinitesimal generators, and thereby ensures that curve evolution takes place on an orbit of the chosen transformation group while remaining a descent equation of the original functional. The main advantage of our approach is that it does not necessitate any knowledge of nor any modification to the original curve functional and is extremely straightforward to implement. Our approach therefore stands in sharp contrast to other approaches where the curve functional is modified by the addition of geometric penalty terms. We illustrate our algorithm on numerous real and synthetic examples.

Algorithms↗

PubMed2004

Edge detection in ultrasound imagery using the instantaneous coefficient of variation.

The instantaneous coefficient of variation (ICOV) edge detector, based on normalized gradient and Laplacian operators, has been proposed for edge detection in ultrasound images. In this paper, the edge detection and localization performance of the ICOV-squared (ICOVS) detector are examined. First, a simplified version of the ICOVS detector, the normalized gradient magnitude squared, is scrutinized in order to reveal the statistical performance of edge detection and localization in speckled ultrasound imagery. Both the probability of detection and the probability of false alarm are evaluated for the detector. Edge localization is characterized by the position of the peak and the 3-dB width of the detector response. Then, the speckle-edge response of the ICOVS as applied to a realistic edge model is studied. Through theoretical analysis, we reveal the compensatory effects of the normalized Laplacian operator in the ICOV edge detector for edge-localization error. An ICOV-based edge-detection algorithm is implemented in which the ICOV detector is embedded in a diffusion coefficient in an anisotropic diffusion process. Experiments with real ultrasound images have shown that the proposed algorithm is effective in extracting edges in the presence of speckle. Quantitatively, the ICOVS provides a lower localization error, and qualitatively, a dramatic improvement in edge-detection performance over an existing edge-detection method for speckled imagery.

Algorithms↗

PubMed2004

Motion gradient vector flow: an external force for tracking rolling leukocytes with shape and size constrained active contours.

Recording rolling leukocyte velocities from intravital microscopic video imagery is a critical task in inflammation research and drug validation. Since manual tracking is excessively time consuming, an automated method is desired. This paper illustrates an active contour based automated tracking method, where we propose a novel external force to guide the active contour that takes the hemodynamic flow direction into account. The construction of the proposed force field, referred to as motion gradient vector flow (MGVF), is accomplished by minimizing an energy functional involving the motion direction, and the image gradient magnitude. The tracking experiments demonstrate that MGVF can be used to track both slow- and fast-rolling leukocytes, thus extending the capture range of previously designed cell tracking techniques.

Algorithms↗

PubMed2004

Segmentation of the prostate from suprapubic ultrasound images.

We present a technique for semiautomated segmentation of human prostates using suprapubic ultrasound (US) images. In this approach, a speckle reducing anisotropic diffusion (SRAD) is applied to enhance the images and the instantaneous coefficient of variation (ICOV) is utilized for edge detection. Segmentation is accomplished via a parametric active contour model in a polar coordinate system that is tailored to the application. The algorithm initially approximates the prostate boundary in two stages. First a primary contour is detected using an elliptical model, followed by a primary contour optimization using an area-weighted mean-difference binary flow geometric snake model. The algorithm was assessed by comparing the computer-derived contours with contours produced manually by three sonographers. The proposed method has application in radiation therapy planning and delivery, as well as in automated volume measurements for ultrasonic diagnosis. The average root mean square discrepancy between computed and manual outlines is less than the inter-observer variability. Furthermore, 76% of the computer-outlined contour is less than 1 sigma manual outline variance away from "true" boundary of prostate. We conclude that the methods developed herein possess acceptable agreement with manually contoured prostate boundaries and that they are potentially valuable tools for radiotherapy treatment planning and verification.

Algorithms↗

PubMed2003

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.

Administration, Inhalation↗

PubMed2002

Automatic tracking of rolling leukocytes in vivo.

The analysis of instantaneous and average rolling leukocyte velocity is crucial to the study of inflammatory disease. In order to record features associated with leukocyte rolling, the leukocyte position must be tracked, typically by manual observation. Automated tracking of leukocytes is possible for in vitro studies, but not for recordings resulting from intravital experiments. Therefore, we have designed and implemented an image processing system for automated tracking of rolling leukocytes in vivo. The novel image processing techniques used in the tracking system successfully address the four major problems associated with tracking cells in vivo: background movement, severe image noise and clutter, cell deformation and contrast change, and occlusion of the target cell by other structures. We have tested the system in two experimental protocols in which leukocyte rolling is observed in venules of the mouse cremaster muscle with and without TNF-alpha treatment. The automated tracking system was validated by comparing automatically generated displacement and velocity data with data from the same recordings collected manually. The root mean squared error between the computed displacements and the manually measured displacements was less than 12% of the average displacement in TNF-alpha-treated venules. The average velocity error was also less than 12%. For untreated venules, the computed and measured displacements and velocities had an RMSE of less than 8%. The automated tracking system allows one, for the first time, to reliably track rolling leukocytes in vivo, thus eliminating possible investigator bias and increasing throughput.

Animals↗

PubMed2002

Tracking leukocytes in vivo with shape and size constrained active contours.

Inflammatory disease is initiated by leukocytes (white blood cells) rolling along the inner surface lining of small blood vessels called postcapillary venules. Studying the number and velocity of rolling leukocytes is essential to understanding and successfully treating inflammatory diseases. Potential inhibitors of leukocyte recruitment can be screened by leukocyte rolling assays and successful inhibitors validated by intravital microscopy. In this paper, we present an active contour or snake-based technique to automatically track the movement of the leukocytes. The novelty of the proposed method lies in the energy functional that constrains the shape and size of the active contour. This paper introduces a significant enhancement over existing gradient-based snakes in the form of a modified gradient vector flow. Using the gradient vector flow, we can track leukocytes rolling at high speeds that are not amenable to tracking with the existing edge-based techniques. We also propose a new energy-based implicit sampling method of the points on the active contour that replaces the computationally expensive explicit method. To enhance the performance of this shape and size constrained snake model, we have coupled it with Kalman filter so that during coasting (when the leukocytes are completely occluded or obscured), the tracker may infer the location of the center of the leukocyte. Finally, we have compared the performance of the proposed snake tracker with that of the correlation and centroid-based trackers. The proposed snake tracker results in superior performance measures, such as reduced error in locating the leukocyte under tracking and improvements in the percentage of frames successfully tracked. For screening and drug validation, the tracker shows promise as an automated data collection tool.

Algorithms↗