An e#cient adaptive multigrid level set method for front propagation purposes in three dimensional medical image processing and segmentation is presented. It is able to deal with non sharp segment boundaries. A flexible, interactive modulation of the front speed depending on various boundary and regularization criteria ensure this goal. E#ciency is due to a graded underlying mesh implicitly defined via error or feature indicating values on the cells of the underlying hexahedral grid. A suitable saturation condition ensures an important regularity condition on the resulting adaptive grid. This simplifies the adaptive fast marching method on the compressed data significantly. As an application the segmentation of glioma is considered. Thus the clinician interactively selects a few parameters describing the speed function and a few seed points referring to a single slice of an MRI data set. Then the automatic process of front propagation generates a family of segments corresponding to the evolution of the front in time, from which the clinician finally selects an appropriate segment covered by the gliom. This selection can be based on a visual evaluation of the propagation on a reference slice using the clinicians expert knowledge. Thus, the overall glioma segmentation turns into an e#cient, nearly real time process with intuitive and usefully restricted user interaction.

Abstract—An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the Region-aided Geometric Snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images. Index Terms—Color snakes, deformable contours, geometric snakes, region segmentation, region-aided snakes, weak-edge leakage. I.

The problem of person recognition and verification based on their hand images has been addressed. The system is based on the images of the right hands of the subjects, captured by a flatbed scanner in an unconstrained pose at 45 dpi. In a preprocessing stage of the algorithm, the silhouettes of hand images are registered to a fixed pose, which involves both rotation and translation of the hand and, separately, of the individual fingers. Two feature sets have been comparatively assessed, Hausdorff distance of the hand contours and independent component features of the hand silhouette images. Both the classification and the verification performances are found to be very satisfactory as it was shown that, at least for groups of about five hundred subjects, hand-based recognition is a viable secure access control scheme.

Local image information is crucial for accurate segmentation of images with intensity inhomogeneity. However, image information in local region is not embedded in popular region-based active contour models, such as the piecewise constant models. In this paper, we propose a region-based active contour model that is able to utilize image information in local regions. The major contribution of this paper is the introduction of a local binary fitting energy with a kernel function, which enables the extraction of accurate local image information. Therefore, our model can be used to segment images with intensity inhomogeneity, which overcomes the limitation of piecewise constant models. Comparisons with other major region-based models, such as the piecewise smooth model, show the advantages of our method in terms of computational efficiency and accuracy. In addition, the proposed method has promising application to image denoising. 1.

Inhaled hyperpolarized helium-3 (or use He) 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 He MR images, it is necessary to segment the lung cavities. This is difficult to accomplish using only the hyperpolarized He MR images, so traditional proton ( H) MR images are frequently obtained concurrent with the hyperpolarized He MR examination. Segmentation of the lung cavities from traditional proton ( H) MRI is a necessary first step in the analysis of hyperpolarized He 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 H 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 (PDE's) 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 H 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 He gas as a contrast agent.

The classical active contour model has two basic internal forces: tension and curvature. These forces are included to provide cohension, equal control point spacing, and locally smooth shape. These classical internal forces have undesirable attributes that am in conflict with these original desired characteristics. Tension evenly spaces the control points, but also causes the models to collapse in weak image gradients. Curvature produces locally smooth curvature, but it does so by foming the model toward a straight line. This paper roturns to the original active contour model motivations to reformulate these internal forces. The desired properties am achieved without the introduction of unwanted model behavior A new spacing force and a new constant change in curvature force am introduced and their performance characteristics am discussed. The paper includes experimental results that demonstrate the efficacy and performance of the proposed re formulations.

Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. The research project AGIR (Grid Analysis of Radiological Data) presented in this paper addresses this challenge through a combined approach: on one hand, leveraging the grid middleware through core grid medical services which target the requirements of medical data processing applications; on the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical applications.

Abstract—Parametric active contour models are one of the preferred approaches for image segmentation because of their computational efficiency and simplicity. However, they have a few drawbacks which limit their performance. In this paper, we identify some of these problems and propose efficient solutions to get around them. The widely-used gradient magnitude-based energy is parameter dependent; its use will negatively affect the parametrization of the curve and, consequently, its stiffness. Hence, we introduce a new edge-based energy that is independent of the parameterization. It is also more robust since it takes into account the gradient direction as well. We express this energy term as a surface integral, thus unifying it naturally with the region-based schemes. The unified framework enables the user to tune the image energy to the application at hand. We show that parametric snakes can guarantee low curvature curves, but only if they are described in the curvilinear abscissa. Since normal curve evolution do not ensure constant arc-length, we propose a new internal energy term that will force this configuration. The curve evolution can sometimes give rise to closed loops in the contour, which will adversely interfere with the optimization algorithm. We propose a curve evolution scheme that prevents this condition. Index Terms—Active contour, curve, segmentation, snake, spline.

Mine detection and classification using high-resolution sidescan sonar is a critical technology for mine counter measures (MCM). As opposed to the majority of techniques which require large training data sets, this paper presents unsupervised models for both the detection and the shadow extraction phases of an automated classification system. The detection phase is carried out using an unsupervised Markov random field (MRF) model where the required model parameters are estimated from the original image. Using a priori spatial information on the physical size and geometric signature of mines in sidescan sonar, a detection -orientated MRF model is developed which directly segments the image into regions of shadow, seabottom-reverberation, and object-highlight. After detection, features are extracted so that the object can be classified. A novel co-operating statistical snake (CSS) model is presented which extracts the highlight and shadow of the object. The CSS model again utilizes available a priori information on the spatial relationship between the highlight and shadow, allowing accurate segmentation of the object's shadow to be achieved on a wide range of seabed types. Results are given for both models on real and synthetic images and are shown to compare favorably with other models in this field.

Abstract—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. Index Terms—Active contours, boundary extraction, classification, leukocyte detection, microscopy. I.