Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy even in case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points are extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods

Automatic Identification and Representation of the Cornea–Contact Lens Relationship Using AS-OCT Images

[Abstract] The clinical study of the cornea–contact lens relationship is widely used in the process of adaptation of the scleral contact lens (SCL) to the ocular morphology of patients. In that sense, the measurement of the adjustment between the SCL and the cornea can be used to study the comfort or potential damage that the lens may produce in the eye. The current analysis procedure implies the manual inspection of optical coherence tomography of the anterior segment images (AS-OCT) by the clinical experts. This process presents several limitations such as the inability to obtain complex metrics, the inaccuracies of the manual measurements or the requirement of a time-consuming process by the expert in a tedious process, among others. This work proposes a fully-automatic methodology for the extraction of the areas of interest in the study of the cornea–contact lens relationship and the measurement of representative metrics that allow the clinicians to measure quantitatively the adjustment between the lens and the eye. In particular, three distance metrics are herein proposed: Vertical, normal to the tangent of the region of interest and by the nearest point. Moreover, the images are classified to characterize the analysis as belonging to the central cornea, peripheral cornea, limbus or sclera (regions where the inner layer of the lens has already joined the cornea). Finally, the methodology graphically presents the results of the identified segmentations using an intuitive visualization that facilitates the analysis and diagnosis of the patients by the clinical experts.This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Moreover, this work has received financial support from the European Union (European Regional Development Fund—ERDF) and the Xunta de Galicia, Grupos de Referencia Competitiva, Ref. ED431C 2016-047.Xunta de Galicia; ED431C 2016-047

Combining experimental volcanology, petrology and geophysical monitoring techniques

In general, an understanding of the complex processes acting before and during volcanic eruptions is approached from various different sides, e.g. laboratory experiments on fragmentation and/or bubble burst eruption mechanisms, petrological analysis of the eruptive products and various geophysical monitoring and source localization techniques. Each of these techniques can deliver valuable insights by adding pieces of information about the physical processes that drive the volcanic activity. However, often studies are focussing on a single aspect of the process, without setting the results in a more general context. Often, this strategy is absolutely valid, when the focus is laid on a single piece in the complex chain of processes taking place in volcanic eruptions. This must fail when the results aim to suggest a valid model for the combined observations at volcanoes using the above described techniques. The resulting models of volcanic source mechanisms and eruptive features can therefore lead to biased assumptions. This study aims to close this gap between laboratory experiments, petro-chemical analysis and modern geophysical monitoring and source localization techniques in a case study of Mt. Yasur (Vanuatu) volcano. The presented laboratory experiments on explosive volcanic eruptions upon rapid decompression show that decompression rate is the dening parameter in the experiments and that a scaling to large-scale processes is valid. Furthermore, a model is presented that correlates measured particle velocities to decompression rate and initial gas-overpressure. This model is used to estimate source volumes and overpressures at Volcan de Colima (Mexico) and Mt. Yasur (Vanuatu). A petrographically and geochemically characterization of Mt. Yasurs eruptive products suggests a shallow magma-mingling process at both of Mt. Yasurs active craters, perhaps due to rejuvenation of material slumped from the crater walls into an open conduit system. A study on the time-reversal imaging technique and its ability to detect the details of finite rupture (or time-variant) processes shows that the limitations of TR imaging start where the source stops being point-localised with respect to the used wavelength. Inversion of the source mechanisms of Strombolian explosions at Mt. Yasur are performed using a multi-parameter dataset consisting of seismic, acoustic and Doppler-radar data. Time-reversal imaging and moment tensor inversion are used to invert the source location of the seismic long-period (f < 1Hz) signals, which is supposed to refl ect fluid movement at depth. The source is located in the north-east of the crater region in a depth of several hundred meters. Furthermore, the source volume of the radiated infrasound signals is estimated from fundamental resonance frequencies. The results showed that the maximum particle velocity measured with the Doppler radar correlates nicely with the estimated source volumes lengths. The inverted seismic moment does not show any correlation with the estimated slug sizes, i.e. the slug size does not map in seismic moment. This is an important information, as it states that a larger source volume does not necessarily produces a larger seismic moment. From these combined results, a common feeder system for all active craters at Mt. Yasur is proposed. The differences in event recurrence rate at the three active craters are believed to be controlled by either the conduit geometry or variations in degassing or cooling rate. Strombolian-type eruptions at Mt. Yasur are suggested to be due to the burst of gas slugs with lengths and overpressures comparable to volcanoes showing similar eruptive patterns. The results illustrate the importance of combined studies that overcome the limitations of single disciplines. In this way, a more comprehensive view of volcanic eruptions and the associated observations is possible. Such a multi-disciplinary approach will contribute to a better understanding of volcanic processes and the associated hazards

Geodesic Active Fields:A Geometric Framework for Image Registration

Image registration is the concept of mapping homologous points in a pair of images. In other words, one is looking for an underlying deformation field that matches one image to a target image. The spectrum of applications of image registration is extremely large: It ranges from bio-medical imaging and computer vision, to remote sensing or geographic information systems, and even involves consumer electronics. Mathematically, image registration is an inverse problem that is ill-posed, which means that the exact solution might not exist or not be unique. In order to render the problem tractable, it is usual to write the problem as an energy minimization, and to introduce additional regularity constraints on the unknown data. In the case of image registration, one often minimizes an image mismatch energy, and adds an additive penalty on the deformation field regularity as smoothness prior. Here, we focus on the registration of the human cerebral cortex. Precise cortical registration is required, for example, in statistical group studies in functional MR imaging, or in the analysis of brain connectivity. In particular, we work with spherical inflations of the extracted hemispherical surface and associated features, such as cortical mean curvature. Spatial mapping between cortical surfaces can then be achieved by registering the respective spherical feature maps. Despite the simplified spherical geometry, inter-subject registration remains a challenging task, mainly due to the complexity and inter-subject variability of the involved brain structures. In this thesis, we therefore present a registration scheme, which takes the peculiarities of the spherical feature maps into particular consideration. First, we realize that we need an appropriate hierarchical representation, so as to coarsely align based on the important structures with greater inter-subject stability, before taking smaller and more variable details into account. Based on arguments from brain morphogenesis, we propose an anisotropic scale-space of mean-curvature maps, built around the Beltrami framework. Second, inspired by concepts from vision-related elements of psycho-physical Gestalt theory, we hypothesize that anisotropic Beltrami regularization better suits the requirements of image registration regularization, compared to traditional Gaussian filtering. Different objects in an image should be allowed to move separately, and regularization should be limited to within the individual Gestalts. We render the regularization feature-preserving by limiting diffusion across edges in the deformation field, which is in clear contrast to the indifferent linear smoothing. We do so by embedding the deformation field as a manifold in higher-dimensional space, and minimize the associated Beltrami energy which represents the hyperarea of this embedded manifold as measure of deformation field regularity. Further, instead of simply adding this regularity penalty to the image mismatch in lieu of the standard penalty, we propose to incorporate the local image mismatch as weighting function into the Beltrami energy. The image registration problem is thus reformulated as a weighted minimal surface problem. This approach has several appealing aspects, including (1) invariance to re-parametrization and ability to work with images defined on non-flat, Riemannian domains (e.g., curved surfaces, scalespaces), and (2) intrinsic modulation of the local regularization strength as a function of the local image mismatch and/or noise level. On a side note, we show that the proposed scheme can easily keep up with recent trends in image registration towards using diffeomorphic and inverse consistent deformation models. The proposed registration scheme, called Geodesic Active Fields (GAF), is non-linear and non-convex. Therefore we propose an efficient optimization scheme, based on splitting. Data-mismatch and deformation field regularity are optimized over two different deformation fields, which are constrained to be equal. The constraint is addressed using an augmented Lagrangian scheme, and the resulting optimization problem is solved efficiently using alternate minimization of simpler sub-problems. In particular, we show that the proposed method can easily compete with state-of-the-art registration methods, such as Demons. Finally, we provide an implementation of the fast GAF method on the sphere, so as to register the triangulated cortical feature maps. We build an automatic parcellation algorithm for the human cerebral cortex, which combines the delineations available on a set of atlas brains in a Bayesian approach, so as to automatically delineate the corresponding regions on a subject brain given its feature map. In a leave-one-out cross-validation study on 39 brain surfaces with 35 manually delineated gyral regions, we show that the pairwise subject-atlas registration with the proposed spherical registration scheme significantly improves the individual alignment of cortical labels between subject and atlas brains, and, consequently, that the estimated automatic parcellations after label fusion are of better quality