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Item Kernel-Based Sampling of Arbitrary Data(The Eurographics Association, 2020) Cammarasana, Simone; Patanè, Giuseppe; Biasotti, Silvia and Pintus, Ruggero and Berretti, StefanoPoint sampling is widely used in several Computer Graphics' applications, such as point-based modelling and rendering, image and geometric processing. Starting from the kernel-based sampling of signals defined on a regular grid, which generates adaptive distributions of samples with blue-noise property, we specialise this sampling to arbitrary data in terms of dimension and structure, such as signals, vector fields, curves, and surfaces. To demonstrate the novelties and benefits of the proposed approach, we discuss its applications to the resampling of 2D/3D domains according to the distribution of physical quantities computed as solutions to PDEs, and to the sampling of vector fields, 2D curves and 3D point sets. According to our experiments, the proposed sampling achieves a high approximation accuracy, preserves the features of the input data, and is computationally efficient.Item SemAnatomy3D: Annotation of Patient-Specific Anatomy(The Eurographics Association, 2015) Banerjee, Imon; Patanè, Giuseppe; Spagnuolo, Michela; Andrea Giachetti and Silvia Biasotti and Marco TariniIn the digital age of medicine, patient-specific 3D anatomical reconstructions are becoming increasingly relevant in several applications, starting from bio-mechanical simulation, virtual surgery, implant design to computerassisted diagnosis. While problems related to imaging and 3D reconstruction have been largely resolved by the advancement in technologies, tools for extracting, coding, sharing and retrieving the semantic content of the patient-specific 3D models are still far from being satisfactory. In this context, we propose SemAnatomy3D framework that aims to bridge the semantic gap between patient-specific 3D geometry and formalized domain knowledge for making the semantics more usable for the definition of patient-specific atlas of anatomy. The purpose of this paper is to describe primary components of the framework. We specialized our framework for the carpal region, but, in principle, it can support similar tasks for other anatomical districts.Item Grontocrawler: Graph-Based Ontology Exploration(The Eurographics Association, 2015) Agibetov, Asan; Patanè, Giuseppe; Spagnuolo, Michela; Andrea Giachetti and Silvia Biasotti and Marco TariniBiomedical ontologies helps discover hidden semantic links between heterogeneous and multi-scale biomedical datasets. Computational methods to ontology analysis may provide a semantic flavor to data analysis of biomedical mathematical models and help discover hidden links. In this paper we present Grontocrawler - a framework for visual ontology exploration applied to the biomedical domain. We define an OWL sublanguage - L and we present a methodology for transformation of L ontologies into directed labelled graphs. We then show how Social Network Analysis techniques (e.g., centrality measures, graph partitioning, community detection) can be used to i) filter the information presented to the user, and ii) provide a summary of knowledge encoded in the ontology. Finally, we show the application of ontology exploration in the biomedical domain to help discover hidden links between the biomedical datasets.Item Comparison and Integration of Erosion Evaluation Methods for Rheumatic Degenerative Diseases(The Eurographics Association, 2020) Paccini, Martina; Patanè, Giuseppe; Spagnuolo, Michela; Biasotti, Silvia and Pintus, Ruggero and Berretti, StefanoMonitoring the development of degenerative rheumatic diseases is at the core of an efficient medical evaluation of the patient. Acquiring information on the pathology progression, indeed, helps to personalize the therapy in order to slow the pathology degeneration. Follow-up exams allow medical doctors to evaluate the situation of the patient over time. Through medical imaging scans, these exams help to identify erosion processes, which are typical indicators of a rheumatic illness. The paper presents a comparison between different methods aiming to identify erosion sites in follow-up exams. In particular, geometricbased and texture-based approaches are compared in terms of extracted information and achieved results. Finally, these two approaches are integrated in order to achieve a more complete analysis of the input anatomical district and of the underlying pathology.