The completion of interrupted lines or the enhancement of flow-like structures is a challenging task in computer vision, human vision, and image processing. We address this problem by presenting a multiscale method in which a nonlinear diffusion filter is steered by the so-called interest operator (second-moment matrix, structure tensor). An m-dimensional formulation of this method is analysed with respect to its well-posedness and scale-space properties. An efficient scheme is presented which uses a stabilization by a semi-implicit additive operator splitting (AOS), and the scale-space behaviour of this method is illustrated by applying it to both 2-D and 3-D images.

This paper describes computer vision algorithms to assist in retinal laser surgery, which is widely used to treat leading blindness causing conditions but only has a 50% success rate, mostly due to a lack of spatial mapping and reckoning capabilities in current instruments. The novel technique described here automatically constructs a composite (mosaic) image of the retina from a sequence of incomplete views. This mosaic will be useful to opthalmologists for both diagnosis and surgery. The new technique goes beyond published methods in both the medical and computer vision literatures because it is fully automated, models the patient-dependent curvature of the retina, handles large interframe motions, and does not require calibration. At the heart of the technique is a 12-parameter image transformation model derived by modeling the retina as a quadratic surface and assuming a weak perspective camera, and rigid motion. Estimating the parameters of this transformation model requires robus...

This paper presents the implementation and quantitative evaluation of a multiphase three-dimensional deformable model in a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to minimize the sensitivity of the method to initial conditions while avoiding the need for aprioriinformation. This random initialization ensures robustness of the method with respect to the initialization and the minimization set up. Postprocessing corrections with morphological operators were applied to refine the details of the global segmentation method. A clinical study was performed on a database of 10 adult brain MRI volumes to compare the level set segmentation to three other methods: “idealized ” intensity thresholding, fuzzy connectedness, and an expectation maximization classification using hidden Markov random fields. Quantitative evaluation of segmentation accuracy was performed with comparison to manual segmentation computing true positive and false positive volume fractions. A statistical comparison of the segmentation methods was performed through a Wilcoxon analysis of these error rates and results showed very high quality and stability of the multiphase three-dimensional level set method. Copyright © 2007 Elsa D. Angelini et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly