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In this paper, detection of edges in oriented fields is addressed. In some applications such as vessel segmentation because of the intrinsic orientation of the structures, edge detection is only demanded in a particular subspace. This is specially usefull when a curve evolution is chosen for segmentation since gradients in parallel to vessel orientation may stop the contour. An anisotropic edge detection scheme is generalized on a Riemannian manifold using the local structure tensor. The method is the generalization of the PLUS operator proposed in [8] for accurate curved edge detection. Examples are given and the comparison is made with the state-of-the-art flux maximizing flow which indicates that significant improvements in terms of leakage minimization and thiner vessel delineation is achievable using our methodology. [ABSTRACT FROM AUTHOR]

In this paper, detection of edges in oriented fields is addressed. Haralick edge detection is an accurate scheme for estimation of the edge in a Euclidean space. However, in some applications such as edge detection for vessel segmentation because of the intrinsic orientation of structures, accuracy is only demanded in a particular subspace. This is specially usefull when a curve evolution is chosen for segmentation since gradients in parallel to vessel orientation stops evolution. Haralick edge detection is generalized on a Riemannian space using the inner product of the vectors under a space metric tensor. This eliminates the spurious gradients and preserves the accuracy on the vessel border. Examples are given and the comparison is made with the state-of-the-art flux maximizing flow indicating that significant improvements in terms of leakage minimization and thiner vessel delineation is achievable using our methodology. [ABSTRACT FROM AUTHOR]

Evolutionary schemes based on the level set theory are effective tools for medical image segmentation. In this paper, a new variational technique for edge integration is presented. Region statistical measures and orientation information from ramp-like edges, are fused within an energy minimization scheme that is based on a new interpretation of edge concept. A region driven advection term simulating the edge strength effect is directly obtained from this minimization strategy. We have applied our method to several real Magnetic Resonance Angiography data sets and comparison has been made with a state-of-the-art vessel segmentation method. Presented results indicate that using this method a significant improvement is achievable and the method can be an effective tool to extract vessels in MRA intracranial images. [ABSTRACT FROM AUTHOR]

Segmentation of CSF and pulsative blood flow, based on a single phase contrast MRA (PC-MRA) image can lead to imperfect classifications. In this paper, we present a novel automated flow segmentation method by using PC-MRA image series. The intensity time series of each pixel is modeled as an autoregressive (AR) process and features including the Linear Prediction Coefficients (LPC), covariance matrix of LPC and variance of prediction error are extracted from each profile. Bayesian classification of the feature space is then achieved using a non-Gaussian likelihood probability function and unknown parameters of the likelihood function are estimated by a generalized Expectation-Maximization (EM) algorithm. The efficiency of the method evaluated on both synthetic and real retrospective gated PC-MRA images indicate that robust segmentation of CSF and vessels can be achieved by using this method. [ABSTRACT FROM AUTHOR]