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OBJECTIVE: The paper proposes a non-invasive original methodology to directly and automatically identify the spine line from the external position of vertebral apophyses, which are key anatomical landmarks. METHODS: Apophyses are detected directly on discrete high-density geometric models of human backs acquired by a 3D scanner. The methodology is inspired by the posturologist's approach that detects the spine line through the identification, by manual palpation, of the spinal apophyses. For this purpose, an appropriate shape index is used to identify vertebral positions. The shape index estimates the local differential geometric properties of the back surface. This index is very discriminating in locating both pronounced and blurred apophyses. To validate the method, the research involved the analysis of 21 healthy human backs acquired in both standing and asymmetric postures. For each of them, a skilled operator detected the spinal apophyses by tactile investigation and located them through cutaneous marking. Markers have been used as the reference for spinal apophyses' positions. RESULTS: A comparison of the proposed approach with state-of-the-art methods has been conducted. This study evidences the high accuracy of the methodology proposed here and the capability to recognize also blurred apophyses. CONCLUSION: The method automatically performs the spine line identification and accurately locates apophyses along both vertical and coronal directions. SIGNIFICANCE: The proposed inexpensive and easy-to-use approach significantly advances over other non-invasive methods. Its ability to detect the apophyses' location potentially offers new capabilities in detecting, diagnosing, and monitoring spinal disorders.

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Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel® RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.

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BACKGROUND AND OBJECTIVE: Because of the three-dimensional distribution of morphological features of human vertebrae and the whole spine, in recent years, to make more precise diagnoses and to design optimized surgical procedures, new protocols have been proposed based on analysing their three-dimensional (3D) models. In the related literature, processes of segmentation and morphological features recognition are essentially performed by a skilled operator that selects the interesting areas. So, being affected by the preparation and experience of the operator, this produces an evaluation that is poorly reproducible and repeatable for the uncertainties of a typical manual measurement process. METHODS: To overcome this limitation, in this paper a new automatic method is proposed for feature segmentation and recognition of human vertebrae. The proposed computer-based method, starting from 3D high density discretized models of thoracic and lumbar vertebrae, automatically performs both the semantic and geometric segmentation of their morphological features. The segmentation and recognition rules codify some important definitions used in the traditional manual method, considering all the vertebra morphology information that is invariant inter-subject. RESULTS: The automatic method proposed here is verified by analysing many real vertebrae, both acquired using a 3D scanner and coming from Computerized Tomography (CT) scans. The obtained results are critically discussed and compared with the traditional manual methods for vertebra analysis. The method has proven to be robust and reliable in the segmentation and recognition of morphological features of vertebrae. Furthermore, the proposed automatic method avoids the blurring of quantitative parameters get from vertebrae, resulting from poor repeatability and reproducibility of manual methods used in the state-of-the-art. CONCLUSIONS: Starting from the automatic segmentation and recognition here proposed, it is possible to automatically calculate the parameters of thoracic or lumbar vertebrae used in archaeology, medicine, or biomechanics or define their new ones.

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From archaeological excavations, huge quantities of material are recovered, usually in the form of fragments. Their correct interpretation and classification are laborious and time-consuming and requires measurement, analysis and comparison of several items. Basing these activities on quantitative methods that process 3D digital data from experimental measurements allows optimizing the entire restoration process, making it faster, more accurate and cheaper. The 3D point clouds, captured by the scanning process, are raw data that must be properly processed to be used in automatic systems for the analysis of archeological finds. This paper focuses on the integration of a shape feature recognizer, able to support the semantic decomposition of the ancient artifact into archaeological features, with a structured database, able to query the large amount of information extracted. Through the automatic measurement of the dimensional attributes of the various features, it is possible to facilitate the comparative analyses between archaeological artifacts and the inferences of the archaeologist and to reduce the routine work. Here, a dedicated database has been proposed, able to store the information extracted from huge quantities of archaeological material using a specific shape feature recognizer. This information is useful for making comparisons but also to improve the archaeological knowledge. The database has been implemented and used for the identification of pottery fragments and the reconstruction of archaeological vessels. Reconstruction, in particular, often requires the solution of complex problems, especially when it involves types of potsherds that cannot be treated with traditional methods.

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The construction of the artificial emissary of Fucino Lake is one of the most ambitious engineering buildings of antiquity. It was the longest tunnel ever made until the 19th century and, due to the depth of the adduction inlet, it required a monumental and complex incile, which, for functionality, cannot be compared to other ancient emissaries. The Roman emissary and its "incile" (Latin name of the inlet structure) were almost completely destroyed in the 19th century, when Fucino Lake was finally dried. Today, only few auxiliary structures such as wells, tunnels, and winzes remain of this ancient work. As evidence of the ancient incile remains a description made by those who also destroyed it and some drawings made by travelers who, on various occasions, visited the site. This paper presents a virtual reconstruction of the Roman incile, obtained both through the philological study of the known documentation, interpreting iconographic sources that represent the last evidence of this structure, and through the survey on the territory. The main purpose is to understand its technical functionalities, the original structures, and its evolution during the time, taking into account the evolution of the Fucino Lake water levels, technological issues, and finally offering its visual reconstruction.