Carbon Nanofiber Type and Content Dependence of the Physical Properties of Carbon Nanofiber Reinforced Polypropylene Composites

The variation of the physical properties of four differ- ent carbon nanofibers (CNFs), based-polymer nano- composites incorporated in the same polypropylene (PP) matrix by twin-screw extrusion process was investigated. Nanocomposites fabricated with CNFs with highly graphitic outer layer revealed electrical isolation-to-conducting behaviors as function of CNF’s content. Nanocomposites fabricated with CNFs with an outer layer consisting on a disordered pyro- litically stripped layer, in contrast, revealed better mechanical performance and enhanced thermal sta- bility. Further, CNF’s incorporation into the polymer increased the thermal stability and the degree of crystallinity of the polymer, independently on the filler content and type. In addition, dispersion of the CNFs’ clusters in PP was analyzed by transmitted light opti- cal microscopy, and grayscale analysis (GSA). The results showed a correlation between the filler concentration and the variance, a parameter which measures quantitatively the dispersion, for all composites. This method indicated a value of 1.4 vol% above which large clusters of CNFs cannot be dispersed effectively and as a consequence only slight changes in mechanical performance are observed. Finally, this study establishes that for tailoring the physical properties of CNF based-polymer nanocomposites, both adequate CNFs structure and content have to be chosen.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.