Abstract: A technique for volumetric blood flow measurement was developed by combining standard Doppler measurements with grey-scale decorrelation. Steered Doppler is used to determine the in-plane velocities, which are then used to extract the out-of-plane velocities from the temporal A-line decorrelation. As a result, a three-dimensional (3-D) vector flow field can be computed over the imaging plane using a single clinical transducer without knowledge of the vessel orientation. Volume flow is computed by integrating the out-of-plane flow over the vessel cross-section. The algorithm was tested using a scattering-enhanced fluid in a 6.4-mm diameter dialysis tubing. For a wide range of transducer angles, the volume flow was accurately measured to within 28% in these preliminary tests.

Abstract: We propose a method for shape description of objects in color images. Our method employs angular maps in order to identify significant changes of color within the image, which are then used to drive snake models. To obtain an angular map, the angle values of the vectors corresponding to color image pixels are first computed with respect to a reference vector, and organized in a two-dimensional matrix. To identify significant color changes within the original image, the edges of the angular map are next extracted. The resulting edge map is then presented to a snake model. Distance and gradient vector flow snake models have been employed in this work. Experimental results show, not only that the resulting object shape descriptions are accurate and quite similar, but also that our method is computationally efficient and flexible.

Abstract: A new scheme in which a snake model is used for object contour detection is proposed. By developing a no-search movement scheme, accepting the effective gradient vector flow field as the contracting force, and adjusting the weighting parameters automatically, an algorithm that is fast, less sensitive to initial contour conditions and accurate in approaching concave parts of an object boundary is obtained.

Abstract: In this paper, an efficient scheme for video object segmentation is proposed. The scheme is based on a multiresolution Gradient Vector Flow field (M-GVF) and a Motion Geometric Space (MGS) formulation. In particular the proposed scheme is initialized from an object approximation which can be provided either (a) automatically (unsupervised case) based on a depth map estimation method or (b) semi-automatically by user interaction. In the following, several feature points are estimated on the initial object contour (i.e. depth object) and an M-GVF adapted MGS is created to determine the direction that a feature point is allowed to move to. In this framework, each feature point moves onto its MGS in order to locate the contour of the physical video object. Experimental results are presented to indicate the reliable performance of the proposed scheme on real life stereoscopic and monocular video sequences.

Abstract: Ultrasound vector flow estimation ben be complicated by the spatial anisotropy of the ultrasound pulse, a limitation most commonly characterized in terms of the different spatial resolutions achieved along the three axes of a typical imaging system. For vector Doppler or spatial quadrature flow estimation techniques, this anisotropy limits, relative to the axial modulation, the maximum achievable frequency of the modulation associated with lateral motion. This anisotropy has one advantage in establishing higher aliasing thresholds in the non-axial dimensions than are typically encountered axially. However, it is likely that one of the obstacles to practical implementation of vector velocity estimation will be the attenuation of non-axial modulation components by conventional wall and clutter filters. As part of our investigation of this topic, we describe a commonality among several vector flow estimators: the vector Dopple described by Overbeck, et. al, the estimator described by Jensen and Munk, and the heterodyning spatial quadrature estimator we have previously described. When implemented with the same transducer array, these estimators produce very similar spatial impulse responses despite the use of radically different complex apodization schemes. In two cases this common response is not formed during beamforming but rather after post-processing has been applied. We describe this isomorphism and discuss the differences among the estimators that we expect to become evident under realistic imaging conditions. We present an analysis of the multi-dimensional spatial frequency responses of these vector velocity estimators in comparison with typical wall filter responses. The axial/lateral anisotropy for all three estimators was found to be on the order of 10 in a pulse-echo regime. This implies that detection of lateral flow at a rate of, for example, 10 m/s would require a wall filter setting providing an axial cut-off velocity of less than 1 m/s. The practical implications of these findings and alternative approaches are discussed.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Abstract: For image segmentation, traditional snake algorithms are often short of the requirement of human interaction and capability in processing multiple objects simultaneously. Watershed techniques however have the drawback of over-segmentation. A new region growing algorithm based on the extended gradient vector flow (E-GVF) field model is proposed for multiple object segmentation. The proposed force field propagates gradient information of object boundaries and provides a good feature for region growing. We perform scoring and selection of seeds by considering their local gradient direction information. This step is automatic and requires no human interaction, making our algorithm suitable for applications. Experiments show that our algorithm is noise-resistant and also resolves the abovementioned drawbacks for snakes and watershed methods. We have tested our algorithm in segmenting multiple objects from realistic and even medical CT images and gained good results.

Abstract: In this paper constrained gradient vector flow (GVF) field generation is performed, for fast and accurate unsupervised stereoscopic semantic segmentation. The scheme utilizes the information provided by a depth segments map, produced by stereo analysis methods and incorporation of a segmentation algorithm. Then a Canny edge detector is applied to the depth region and produces an edge map. The edge map is used for tube estimation inside which the GVF field evolves. After generation of the GVF field an active contour is unsupervisedly initialized onto the outer bound of the tube. Finally a greedy approach is adopted and the active contour, guided by the GVF field, extracts the VOP. Experimental results on real life stereoscopic video sequences indicate the efficiency of the proposed scheme.

Abstract: This paper proposes a new front propagation flow for boundary extraction. The proposed framework is inspired by the geodesic active contour model and leads to a paradigm that is relatively free from the initial curve position. Towards this end, it makes use of a recently introduced external boundary force, the gradient vector field that refers to a spatial diffusion of the boundary information. According to the proposed flow, the traditional boundary attraction term is replaced with a new force that guides the propagation to the object boundaries from both sides. This new geometric flow is implemented using a level set approach, thereby allowing dealing naturally with topological changes and important shape deformations. Moreover the level set motion equations are implemented using a recently introduced numerical approximation scheme, the Additive Operator Splitting Schema (AOS) which has a fast convergence rate and stable behavior. Encouraging experimental results are provided using real images.