Titlebook: Imaging Methods for Novel Materials and Challenging Applications, Volume 3; Proceedings of the 2 Helena Jin,Cesar Sciammarella,Sanichiro Yo

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Thermal Infrared Imaging to Study Microstructural Damage and Failure in Aluminum Alloys,n-situ during tensile and notched tensile experiments at the microstructural grain level. Specimen preparation and imaging techniques are described. The results are anticipated to guide and improve alloy-specific damage evolution constitutive models to enable improved deformation and failure predict

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Quantitative Evaluation of the Nano-Scaled Polymeric Film System,. Generally the materials used to the nano-scaled thin film systems have different mechanical properties comparing to the conventional bulk materials. Therefore, it is important to measure the mechanical properties for the nano-scaled thin film. Especially the properties of the pre-treated surface o

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Evaluation of Dynamic Deformation Behavior of Aluminum Alloy by Electronic Speckle Pattern Interferhe specimens prepared were aged to a peak hardness and over-aged, and the images of displacement contours consisted of fringes (fringe pattern) in the elastic/plastic deformation were compared. The fringe patterns were classified by the deformation process. In elastic region, the fringe pattern of l

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tion. However, most of existing brain tumor segmentation methods based on deep learning are not able to ensure appearance and spatial consistency of segmentation results. In this study we propose a novel brain tumor segmentation method by integrating a Fully Convolutional Neural Network (FCNN) and C

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Sanichiro Yoshidavalidate new registration, segmentation, and other image processing algorithms. In this work we present a collection of data from tumour patients acquired at the Montreal Neurological Institute and Hospital that will be released as a publicly available dataset to the image processing community. The

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Nils G. Ohlsonical trials. The automatic detection and segmentation of these enhanced pathologies remains a difficult challenge, as they can be very small and visibly similar to other non-pathological enhancements (e.g. blood vessels). In this paper, we propose a deep neural network classifier for the detection a

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Miguel León-Rodríguez,Ramón Rodríguez-Vera,Juan A. Rayas,Sergio Calixtoetworks for the prediction of the follow-up tissue outcome in strokes are, however, not yet accurate enough or capable of properly modeling the growth mechanisms of ischaemic stroke..In our previous shape space interpolation approach, the prediction of the follow-up lesion shape has been bounded usi