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Poisson inverse gradient (PIG) initialization method is a novel automatic initialization approach for parametric active models. Results suggest the effectiveness of the PIG method in terms of robustness to noise, accommodation of broken edges and multiple objects, accurate segmentation and rapid convergence to features of interest.

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. Index Terms--B-splines, cartilage surface extraction, gradient vector flow, segmentation, snakes.

Inclusion filters are a class of connected operators that fill or retain the holes of the connected sets depending on application-specific criteria. 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 are important applications of the inclusion filters.

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. Index Terms--Active contours, inflammation research, segmentation, spatiotemporal image analysis.

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 [micro]m. 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 [micro]m. In 10 sequences from different venules, we were able to track 11 cells using an active contour approach over moving FOVs. Index Terms--Active contour, leukocyte, registration, stabilization.

The edge detection and localization performance of the instantaneous coefficient of variation (ICOV)-squared detector is examined. The experiments with real ultrasound images show that the proposed algorithm is effective in extracting edges in the presence of speckle.

A novel approach to constraining the evolution of active contours used in image analysis is presented. The main advantage of this approach is that it does not necessitate any knowledge of nor any modification to the original curve functional and is extremely straightforward to implement.

The use of level set analysis from threshold decomposition technique for leukocyte detection as it captures multiple cells in a single frame, and its tracking using spatial and shape coherency of the cells within the microvasculature is discussed. The results of successful detection and tracking of rolling leukocytes using the energy functional and level set approach in a number of digital video sequences are presented.

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. Index Terms--Active contour model, B-splines, gradient vector flow, intravital microscopy, snakes, tracking, vessel boundary detection.

The derivation of speckle reducing anisotropic diffusion (SRAD) is presented. SRAD is a diffusion method for ultrasonic and radar imaging applications.

A study was conducted on a new approach to image estimation based on a flexible constraint framework that shows meaningful structural image assumptions. The findings indicate that approximate solutions may be computed in reasonable time using the novel generalized deterministic annealing optimization technique. Moreover, the findings demonstrate high quality image estimation as measured by local feature integrity, and improvement in signal-to-noise ratio.

Multigrid anisotropic diffusion (MAD) algorithm for imaging processing is introduced as a mechanism to enhance analysis and formalism to the hierarchical diffusion technique. However, MAD is not limited to a single realization of the diffusion coefficient. Instead, the performance of fixed-resolution and multigrid mechanisms are evaluated by three different solutions for the diffusion coefficients which represent three groups of anisotropic diffusion techniques.

We develop a general formalism for computing high quality, low-cost solutions to nonconvex combinatorial optimization problems expressible as distributed interacting local constraints. For problems of this type, generalized deterministic annealing (GDA) avoids the performance-related sacrifices of current techniques. GDA exploits the localized structure of such problems by assigning K-state neurons to each optimization variable. The neuron values correspond to the probability densities of K-state local Markov chains and may be updated serially or in parallel; the Markov model is derived from the Markov model of simulated annealing (SA), although it is greatly simplified. Theorems are presented that firmly establish the convergence properties of GDA, as well as supplying practical guidelines for selecting the initial and final temperatures in the annealing process. A benchmark image enhancement application is provided where the performance of GDA is compared to other optimization methods. The empirical data taken in conjunction with the formal analytical results suggest that GDA enjoys significant performance advantages relative to current methods for combinatorial optimization.

Partial differential equations and AM-FM image modeling was used to achieve an automated method for repairing broken, occluded oriented image textures.

Novel smoothing constraints have been developed for use in the regularized image restoration problem with the aid of two broad classes of physically significant models. The models, called piecewise image models and local image models, capture distinct image properties that can be adapted to the image and that reflect structurally important surface properties. Numerical results are presented to illustrate the mechanism of model selection, image restoration and parameter selection.