Feature Preserving Point Set Surfaces based on Non-Linear Kernel Regression

International audienceMoving least squares (MLS) is a very attractive tool to design effective meshless surface representations. However, as long as approximations are performed in a least square sense, the resulting definitions remain sensitive to outliers, and smooth-out small or sharp features. In this paper, we address these major issues, and present a novel point based surface definition combining the simplicity of implicit MLS surfaces [SOS04,Kol05] with the strength of robust statistics. To reach this new definition, we review MLS surfaces in terms of local kernel regression, opening the doors to a vast and well established literature from which we utilize robust kernel regression. Our novel representation can handle sparse sampling, generates a continuous surface better preserving fine details, and can naturally handle any kind of sharp features with controllable sharpness. Finally, it combines ease of implementation with performance competing with other non-robust approaches

Indirect microfabrication of biomimetic materials for locomotor tissues regeneration

Tissue Engineering is a new field of the scientific research with a final aim to develop techniques for regeneration, repair, maintenance and growth of tissues or organs to overcome the limitations intrinsic to current therapeutic strategies. A fundamental element of this approach is the scaffold. The scaffold is a 2D and 3D structure, made with natural or synthetic material, that emulates the extracellular matrix, that is it offers mechanical, topological, biochemical and chemical stimuli to promote cellular organization, growth and differentiation to create a tissue with adequate functional and morphological characteristic. Scaffolds are therefore characterized by peculiar features (e.g. porosity, mechanical properties) determined by the material and by the manufacture process. Nowadays, the additive Rapid Prototyping (RP) techniques are the best approach to realize complex structures, because overcome all the problem of conventional (subtractive) techniques. Despite the high potential, RP techniques are not always compatible with all materials. In particular, hydrogels, an elective class of biomaterial for scaffolds realization because the lot of features in common with the extracellular matrix, results very difficult to be processed. To overcome these limitations and take advantage of all benefits of rapid prototyping, indirect rapid prototyping (iRP) was developed, that is the realization of scaffold or other structures starting from sacrificial molds realized by RP. The iRP offers the benefits to fabricate composite scaffold realized with different materials, with less waste and high fidelity in the realization of the designed structure. One of the critical aspect of this class of realization process is the extraction of the final object from the mold. A possible solution, proposed in this research, is to realize the mold with low melting point materials, dissolving the mold at the end of the process without damaging the scaffold. Moving in this direction, the attention of this research is focused on two classes of materials, low melting point waxes and agarose. Two alternative RP techniques have been evaluated: new modules of the PAM^2, a continuous flow system, and a inkjet-based device have been designed and realized to test the feasibility of this approach. In addiction, an alternative approach to fabricate agarose microstructure, by exploiting the different agarose gelling ability in DMSO and water, has been proposed. In a future perspective, casting of the desired material, which may include also cells, should be performed directly in the surgery room using an anatomical shaped mold designed on the patient needs. Following this approach, two plugins for bioimages de-noising and segmentation, based on the ITK library, have been implemented for the OsiriX software. To further test the versatility of the two microfabrication devices, other applications have been explored, such as the realization of microfluidic circuits using PAM^2 or printing carbon nanotubes suspension for polymeric actuators

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Automatic 3D model creation with velocity-based surface deformations

The virtual worlds of Computer Graphics are populated by geometric objects, called models. Researchers have addressed the problem of synthesizing models automatically. Traditional modeling approaches often require a user to guide the synthesis process and to look after the geometry being synthesized, but user attention is expensive, and reducing user interaction is therefore desirable. I present a scheme for the automatic creation of geometry by deforming surfaces. My scheme includes a novel surface representation; it is an explicit representation consisting of points and edges, but it is not a traditional polygonal mesh. The novel surface representation is paired with a resampling policy to control the surface density and its evolution during deformation. The surface deforms with velocities assigned to its points through a set of deformation operators. Deformation operators avoid the manual computation and assignment of velocities, the operators allow a user to interactively assign velocities with minimal effort. Additionally, Petri nets are used to automatically deform a surface by mimicking a user assigning deformation operators. Furthermore, I present an algorithm to translate from the novel surface representations to a polygonal mesh. I demonstrate the utility of my model generation scheme with a gallery of models created automatically. The scheme's surface representation and resampling policy enables a surface to deform without requiring a user to control the deformation; self-intersections and hole creation are automatically prevented. The generated models show that my scheme is well suited to create organic-like models, whose surfaces have smooth transitions between surface features, but can also produce other kinds of models. My scheme allows a user to automatically generate varied instances of richly detailed models with minimal user interaction