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5th International conference on Information Technology and Nanotechnology, ITNT 2019

作者： Shirokanev, A.S. Kirsh, D.v. Kupriyanov, A.v. Samara National Research University Moskovskoe Shosse 34A Samara443086 Russia Image Processing Systems Institute of RAS Branch of the FSRC Crystallography and Photonics RAS Molodogvardejskaya street 151 Samara443001 Russia

The study of substances with a crystal structure is a complex multi-step process. The key step in the crystalline substance analysis is the unit cell parameter estimation. The estimation of the crystal lattice unit cell parameters is a particular problem that involves the search of the crystal lattice model's parameters according to the information which can be extracted from the substance. In these recent times, the most accurate information about the substance structure can be obtained with the electron microscope whose linear resolution is high enough to observe the atomic structure of a substance. The problem of parameter estimation in this case means the reconstruction of the three-dimensional crystal lattice with 2-dimentional images received by an electron microscope, and the estimation of the crystal lattice unit cell parameters by reconstructed lattice. In the previous papers the crystal lattice parametric identification algorithms based on solving the local optimization problem were presented. However, the analysis of a large crystal lattice database requires a lot of computations. In this paper, a high-performance crystal lattices parametric identification algorithm using the CUDA technology is proposed. The investigation of the algorithm effectiveness is carried out on the GPU GeForce Nvidia GTX 1070 Ti. With data dimension more than 32 translations the acceleration is higher than 70. The algorithm runs more efficiently at the use of a large number of CUDA-blocks. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Parameter estimation

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Development of the “polar map” method in nuclear cardiology

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AIP conference Proceedings 2021年第1期2351卷

作者： I. P. Kolinko N. v. Denisova 1Novosibirsk State University 630090 Novosibirsk Russia 2Khristianovich Institute of Theoretical and Applied Mechanics SB RAS 630090 Novosibirsk Russia

Despite the widespread use of the SPECT method for the diagnosis of cardiovascular diseases, there are still unresolved problems with this method, which can lead to misinterpretation of the data, and, as a result, wrong diagnosis. At the moment, in diagnostic nuclear medicine there is an active development of an approach based on the method of mathematical modeling. It includes a mathematical model of the distribution of a radiopharmaceutical (RFP) in the body of a virtual patient, a mathematical model for collecting raw data, and a mathematical program for creating a reconstructed image. Using the patient model, we can program the heart disease and present the examination results in the same way as the clinical data. Further, by verifying the obtained images with images obtained from real patients, the error of the reconstruction algorithms can be estimated. In medical practice, the image of the myocardium of the left ventricle (Lv) of the heart in the mode of tomographic slices is often used. This paper presents the results of studies aimed at developing the representation of a mathematical model of the Lv myocardium in the form of a polar map “bulleye”.

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An Efficient Computer Aided Detection for 3D Neurostructural reconstruction of Magnetic Resonance images 9

An Efficient Computer Aided Detection for 3D Neurostructural...

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9th Cairo International Biomedical Engineering conference (CIBEC)

作者： Mabrouk, Mai S. Marzouk, Samir Y. Afify, Heba M. Misr Univ Sci & Technol MUST Univ Dept Biomed Engn Cairo Egypt Arab Acad Sci & Technol Basic & Appl Sci Dept Cairo Egypt Modern Univ Technol & Informat MTI Univ Bioelect Engn Dept Cairo Egypt

ISBN: (纸本)9781538681541

The comprehensive framework for analyzing brain images performs by the integration between three dimensional (3D) reconstruction and neuroimaging approach to realize brain diseases progressions. Computer Aided Detection (CAD) technology has numerous achievements in brain tumor processing for improving the quality of brain visualization to support neuroradiologists without the need for surgical biopsy or resection. Despite the advance in the radiological diagnosis of neuroimaging data, magnetic resonance imaging (MRI) has some restrictions that related to human errors and incomplete interpretation of brain tumor regions. Also, MRI scan produces 2D images of the brain that was very difficult to handle different types of tumor. Therefore, many algorithms are used computer-based classification to accurately distinguish between tumor regions from the brain MR images that provided an early diagnosis of brain diseases. This study investigated the CAD system using 3D image reconstruction of MR brain and tumor structures efficiently under MATLAB platform to recognize the location, volume, and type of brain tumors. In addition, the proposed system applied the Fuzzy C-Means (FCM) algorithm as image segmentation and support vector machine (SvM) as image classification for tumor detection of MR brain images. Results confirmed that this 3D model depicted an advanced view for estimating of human brain diseases.

关键词： Computer-Aided Detection (CAD) Magnetic resonance imaging (MRI) 3D image reconstruction support vector machine (SvM) Fuzzy C-Means (FCM) algorithm

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Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks

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MEDICAL image ANALYSIS 2018年 48卷 162-176页

作者： Lin, Jianyu Clancy, Neil T. Qi, Ji Hu, Yang Tatla, Taran Stoyanov, Danail Maier-Hein, Lena Elson, Daniel S. Imperial Coll London Hamlyn Ctr Robot Surg London England Imperial Coll London Dept Comp London England UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England UCL Ctr Med Image Comp London England UCL Dept Comp Sci London England UCL Dept Surg & Canc London England Northwick Pk Hosp & Clin Res Ctr Dept Otolaryngol Harrow Middx England German Canc Res Ctr Div Med & Biol Informat Heidelberg Germany

Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual modality endoscopic system has been proposed to enable tissue surface shape reconstruction and hyperspectral imaging (HSI). This system centers around a probe comprised of an incoherent fiber bundle, whose fiber arrangement is different at the two ends, and miniature imaging optics. For 3D reconstruction with structured light (SL), a light pattern formed of randomly distributed spots with different colors is projected onto the tissue surface, creating artificial texture. Pattern decoding with a Convolutional Neural Network (CNN) model and a customized feature descriptor enables real-time 3D surface reconstruction at approximately 12 frames per second (FPS). In HSI mode, spatially sparse hyperspectral signals from the tissue surface can be captured with a slit hyperspectral imager in a single snapshot. A CNN based super-resolution model, namely "super-spectral-resolution" network (SSRNet), has also been developed to estimate pixel-level dense hypercubes from the endoscope cameras standard RGB images and the sparse hyperspectral signals, at approximately 2 FPS. The probe, with a 2.1 mm diameter, enables the system to be used with endoscope working channels. Furthermore, since data acquisition in both modes can be accomplished in one snapshot, operation of this system in clinical applications is minimally affected by tissue surface movement and deformation. The whole apparatus has been validated on phantoms and tissue (ex vivo and in vivo), while initial measurements on patients during laryngeal surgery show its potential in real-world clinical applications. (C) 2018 Elsevier B.v. All rights reserved.

关键词： Intra-operative imaging 3D reconstruction Structured light Hyperspectral imaging Deep learning Super-spectral-resolution

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Kinect 3D reconstruction Fusion Mapping with C# Algorithm by RGB-D Sensor

Kinect 3D Reconstruction Fusion Mapping with C# Algorithm by...

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International conference on Advances in Science, Engineering and Robotics Technology

作者： Ashikur Rahman Shoumik Rahman Mehedy Rashedur M. Rahman Dept. of Computer Science and Engineering Brac University Dept. of Electrical and Computer Engineering North South University

ISBN: (纸本)9781728134468

The fundamental objective of this paper is to describe the applications of Microsoft Kinect sensor with robust algorithm and solution to present 3-D surface reconstruction mapping with different measurements. In the line of Sensor operations, there could be two possible categories into 3-D reconstructions such as exorbitant laser scanner and other is by taking photos from various angles. In this paper, we have used indoor environmental scenes by depth sensor camera Microsoft Kinect. data Pre-operations, sensor posture evaluations, computational fusion of the depth sensor data and 3-D surface extractions are the four major part of this experiment. The algorithm of this paper has been built uniquely with C# programming.

关键词： Microsoft kinect sensor 3D reconstruction Depth-sensor camera image processing Algorithms three-dimensional displays algorithms image processing game player Sensors laser scanners three-dimensional surface paper Microsoft

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Integrating data and image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium

Integrating Data and Image Domain Deep Learning for Limited ...

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International conference on Computer vision Workshops (ICCv Workshops)

作者： Muhammad Usman Ghani W. Clem Karl Boston University

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 90 degree limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, non-destructive evaluation, and medical imaging.

关键词： Computed tomography image reconstruction Generators Machine learning data models

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PlaneRCNN: 3D Plane Detection and reconstruction from a Single image

PlaneRCNN: 3D Plane Detection and Reconstruction from a Sing...

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Chen Liu Kihwan Kim Jinwei Gu Yasutaka Furukawa Jan Kautz NVIDIA Simon Fraser University

ISBN: (纸本)9781728132945

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs piecewise planar surfaces from a single RGB image. PlaneRCNN employs a variant of Mask R-CNN to detect planes with their plane parameters and segmentation masks. PlaneRCNN then jointly refines all the segmentation masks with a novel loss enforcing the consistency with a nearby view during training. The paper also presents a new benchmark with more fine-grained plane segmentations in the ground-truth, in which, PlaneRCNN outperforms existing state-of-the-art methods with significant margins in the plane detection, segmentation, and reconstruction metrics. PlaneRCNN makes an important step towards robust plane extraction, which would have an immediate impact on a wide range of applications including Robotics, Augmented Reality, and virtual Reality. Code and data are available at .

关键词： plane Planing Platanus hybrida Reconstructive Surgical Procedures Masks ground truth reconstructions robotics imagery (Psychotherapy) image images

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USCT reference database: conclusions from the first SPIE USCT data challenge and future directions

USCT reference database: conclusions from the first SPIE USC...

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conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Ruiter, Nicole v. Zapf, Michael Hopp, Torsten Gemmeke, Hartmut van Dongen, Koen W. A. Camacho, Jorge Herraiz, Joaquin L. Liva, Mailyn Perez Udias, Jose M. Karlsruhe Inst Technol Inst Data Proc & Elect Karlsruhe Germany Delft Univ Technol Lab Acoust Wavefield Imaging Delft Netherlands Spanish Natl Res Council Ultrasound Syst & Technol Grp Madrid Spain Univ Complutense Madrid Nucl Phys Grp Madrid Spain

ISBN: (数字)9781510616509

ISBN: (纸本)9781510616509;9781510616493

Ultrasound Computer Tomography is an exciting new technology mostly aimed at breast cancer imaging. Due to the complex interaction of ultrasound with human tissue, the large amount of raw data, and the large volumes of interest, both image acquisition and image reconstruction are challenging. Following the idea of open science, the long term goal of the USCT reference database is establishing open and easy to use data and code interfaces and stimulating the exchange of available reconstruction algorithms and raw data sets of different USCT devices. The database was established with freely available and open licensed USCT data for comparison of reconstruction algorithms, and will be maintained and updated. Additionally, the feedback about data and system architecture of the scientists working on reconstruction methods will be published to help to drive further development of the various measurement setups.

关键词： Ultrasound computer tomography data challenge open science open data

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FML: Face Model Learning from videos

FML: Face Model Learning from Videos

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IEEE/CvF conference on Computer vision and Pattern Recognition

作者： Ayush Tewari Florian Bernard Pablo Garrido Gaurav Bharaj Mohamed Elgharib Hans-Peter Seidel Patrick Perez Michael Zollhofer Christian Theobalt MPI Informatics Saarland Informatics Campus Technicolor Valeo.ai Stanford University

ISBN: (纸本)9781728132945

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet. This virtually endless source of training data enables learning of a highly general 3D face model. In order to achieve this, we propose a novel multi-frame consistency loss that ensures consistent shape and appearance across multiple frames of a subject's face, thus minimizing depth ambiguity. At test time we can use an arbitrary number of frames, so that we can perform both monocular as well as multi-frame reconstruction.

关键词： face modeling videotapes images computer vision multiframe Learning video clips TESTING TIME three-dimensional displays Fluorometholone fiber reinforced metal laminates Face Three Dimensions image data FRAMES

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Generative Adversarial Networks Conditioned on Brain Activity Reconstruct Seen images

Generative Adversarial Networks Conditioned on Brain Activit...

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IEEE International conference on Systems, Man, and Cybernetics (SMC)

作者： St-Yves, Ghislain Naselaris, Thomas Med Univ South Carolina Dept Neurosci 96 Jonathan Lucas St CSB 325c Charleston SC 29425 USA Med Univ South Carolina Dept Neurosci 96 Jonathan Lucas St CSB 325h Charleston SC 29425 USA

ISBN: (纸本)9781538666500

We consider the inference problem of reconstructing a visual stimulus from brain activity measurements (e.g. fMRI) that encode this stimulus. Recovering a complete image is complicated by the fact that neural representations are noisy, high-dimensional, and contain incomplete information about image details. Thus, reconstructions of complex images from brain activity require a strong prior. Here we propose to train generative adversarial networks (GANs) to learn a generative model of images that is conditioned on measurements of brain activity. We consider two challenges of this approach: First, given that GANs require far more data to train than is typically collected in an fMRI experiment, how do we obtain enough samples to train a GAN that is conditioned on brain activity? Secondly, how do we ensure that our generated samples are robust against noise present in fMRI data? Our strategy to surmount both of these problems centers around the creation of surrogate brain activity samples that are generated by an encoding model. We find that the generative model thus trained generalizes to real fMRI data measured during perception of images and is able to reconstruct the basic outline of the stimuli.

关键词： Gallium nitride image reconstruction Brain modeling Generative adversarial networks Encoding Training

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