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computer graphics (ACM) 2001年第3期35卷 14-21页

作者： Saupe, Dietmar Alexa, Marc Chesnais, Alain Encarnação, Jose Institut für Informatik Universität Leipzig GRIS Technische Universität Darmstadt University of Leipzig Research Group on Computer Graphics and Image Processing Alias Wavefront 17 bis rue Joseph de Maistre 75018 Paris France Fraunhofer-Institut fuer Graphische Datenverarbeitung Wilhelminenstrasse 7 D-64283 Darmstadt Germany 3D Graphics Computing Department of Computer Science Technische Universität Darmstadt

The various institution and developments in the field of computer graphics in Germany is discussed. The German research Foundation( DFG), as recognition of research in computer graphics, sponsors a special interdisciplinary graduates study program on 3D image analysis and synthesis at the University of Erlangen-Nuremburg. The German Association for Informatics, founded in 1983, brings together about 40 representatives from Universities, colleges and industry that co-ordinate activities form five subgroups specializing in foundations and systems, imaging and visualization, graphical user interfaces, simulation and animation, and geometric modeling. Several companies and forums working in the area of computer graphics are situated in Darmstadt.

关键词： computer graphics

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Automated liver segmentation and steatosis grading using deep learning on B-mode ultrasound images

Automated liver segmentation and steatosis grading using dee...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Vianna, Pedro Kulbay, Merve Boustros, Pamela Calce, Sara-Ivana Larocque-Rigney, Cassandra Patry-Beaudoin, Laurent Luo, Yi Hui Chaudary, Muawiz Kadoury, Samuel Nguyen, Bich Montagnon, Emmanuel Belilovsky, Eugene Wolf, Guy Chassé, Michaël Tang, An Cloutier, Guy Laboratory of Biorheology and Medical Ultrasonics Crchum Canada École Polytechnique de Montréal Canada Chum Department of Pathology Canada Mila - Quebec Ai Institute Canada Concordia University Canada Université de Montréal Department of Computer Sciences and Operations Research Canada Canada Clinical Laboratory of Image Processing Crchum Canada Département de Radiologie Canada Université de Montréal Institute of Biomedical Engineering Canada

ISBN: (纸本)9798350346459

Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screening and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment. © 2023 IEEE.

关键词： Classification (of information)

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Semantic Multimedia 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Yannis Avrithis Yiannis Kompatsiaris Steffen Staab Noel E. O’Connor

ISBN: (数字)9783540493372

ISBN: (纸本)9783540493358

We are delighted to welcome you to the proceedings of the 1st International Conference on Semantic and Digital Media Technologies held in Athens. SAMT 2006 aims to narrowthe large disparity between the low-level descr- tors that can be computed automatically from multimedia content and the ri- ness and subjectivity of semantics in user queries and human interpretations of audiovisual media — The Semantic Gap. SAMT started out as two workshops, EWIMT 2004 and EWIMT 2005, that quickly achieved success in attracting high-quality papers from across Europe and beyond. This year EWIMT turned into the full-?edged conference SAMT, bringing together forums, projects, - stitutions and individuals investigating the integration of knowledge, semantics and low-level multimedia processing, and linking them with industrial engineers who exploit the underlying emerging technology. In total, 68 papers were submitted to the SAMT 2006 conference and each was reviewed by at least two independent reviewers. We are grateful to the membersoftheTechnicalProgramCommitteewhocompletedthesereviewsand allowed us to put together a very strong technical program of 17 papers. The selection process was very competitive with only 25% of papers being selected for oral presentation. The program also included two invited keynote talks from Alan Smeaton and Guus Schreiber, and we are very grateful to them for their insightful presentations.

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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2011 1st Middle East Conference on Biomedical Engineering, MECBME 2011

作者： Hosseini, M. Parsa Soltanian-Zadeh, Hamid Akhlaghpoor, Shahram Department of Electrical Engineering Islamic Azad University Science and Research Branch of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424470006

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs). © 2011 IEEE.

关键词： computerized tomography

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research Progress of Encoder-Decoder Network in image Fusion

SSRN

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SSRN 2023年

作者： Zhou, Tao Zhang, Xiangxiang Cheng, Qianru Li, Qi Lu, Huiling School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China School of Science Ningxia Medical University Yinchuan750004 China

A B S T R A C Timage fusion is a research hotspot in the field of computer vision, which obtains complementary information from the different modal images and then presents it in a comprehensive, highly information-cohesive fused image. Encoder-decoder networks are an important technical method in the field of image fusion. This paper reviewed the research status and development of the Encoder-decoder networks in the field of image fusion from the following aspects: Firstly, the18 image fusion-related datasets and the 22 open-source codes of deep learning-based image fusion are summarized. Secondly, the model structure of encoder-decoder networks and the basic principles for image fusion based on the encoder-decoder network are described. Thirdly, encoder-decoder networks fusion models are summarized into 4 aspects: Convolutional Auto-encoder network(CAE Net), Sparse Auto-encoder network(SAE Net), U-shape Encoder-decoder network(U-net) and Variational Auto-encoder network(VAE Net),in U-net, there are 5 types: U-net incorporating residual connection mechanism (Res U-net), U-net incorporating dense connection mechanism (Dens U-net), U-net incorporating multiscale connection mechanism (MS U-net), U-net incorporating attention mechanism (Att U-net) and U-shaped Encoder-decoder networks(U-net) combined with Generative Adversarial Network(GAN U-net). Fourthly, the existing image fusion evaluation metrics are summarized from both subjective and objective aspects. Fifthly, the application of Encoder-decoder network in 5 kinds of image fusion, such as multi-exposure image fusion, multi-focus image fusion, infrared and visible image fusion, medical image fusion and remote sensing image fusion are summarized. Finally, there is a discussion of the main challenges faced by encoder-decoder networks in the image fusion domain and an outlook on future directions. This paper systematically analyzed the application of encoder-decoder networks in the image fusion areas, which has posi

关键词： Deep learning

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Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection

arXiv

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arXiv 2023年

作者： Wu, Yunan Castro-Macías, Francisco M. Morales-Álvarez, Pablo Molina, Rafael Katsaggelos, Aggelos K. Image and Video Processing Laboratory Department of Electrical and Computer Engineering Northwestern University United States Department of Computer Science and Artificial Intelligence University of Granada Spain Department of Statistics and Operations Research University of Granada Spain University of Granada Spain

Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assumes that instances in each bag are independent samples from a given distribution. However, instances are often spatially or sequentially ordered, and one would expect similar diagnostic importance for neighboring instances. To address this, in this study, we propose a smooth attention deep MIL (SA-DMIL) model. Smoothness is achieved by the introduction of first and second order constraints on the latent function encoding the attention paid to each instance in a bag. The method is applied to the detection of intracranial hemorrhage (ICH) on head CT scans. The results show that this novel SA-DMIL: (a) achieves better performance than the non-smooth attention MIL at both scan (bag) and slice (instance) levels;(b) learns spatial dependencies between slices;and (c) outperforms current state-of-the-art MIL methods on the same ICH test set. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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2011 1st Middle East Conference on Biomedical Engineering, MECBME 2011

ISBN: (纸本)9781424470006

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. © 2011 IEEE.

关键词： Pulmonary diseases

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On the chip implementation of analogic algorithms for optical detection of some layout errors of printed circuit boards

On the chip implementation of analogic algorithms for optica...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： K. Tomordi P. Foldesy P. Szolgay Analogic and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Image Processing and Neurocomputing Department Faculty of Engineering University of Veszprém Veszprem Hungary

Printed circuit board layout inspection methods are mostly based on local geometric information, therefore they are well suited to the cellular neural networks (CNN) paradigm. The wire break, the wire and isolation width violation and an "H" type short circuits detector analogic algorithms were tested on a 20*22 CNN Universal Machine (CNNUM) chip working in the CNN Chip Prototyping System (CCPS) and on the CNN Engine Board (CNNEB), and the results were compared to the commercially available inspection systems.

关键词： Cellular neural networks Inspection Wire Circuit testing Geometrical optics Optical computing Printed circuits Detectors System testing Turing machines

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CNNUM-Based Methods Using Deformable Contours on Smooth Boundaries

CNNUM-Based Methods Using Deformable Contours on Smooth Boun...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： Tamas Szabo Peter Szolgay Department of Image Processing and Neurocomputing Faculty of Information Technology University of Pannonia Veszprem Hungary Analogical and Neural Computing Systems Laboratory Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary

In this paper two methods are presented. A CNNUM-based method is shown to quantify the displacement of the normal interhemisperic bilateral symmetry line. The method uses a deformable open contouring technique. Anothe... 详细信息

关键词： Active contours Computed tomography Magnetic resonance Anatomical structure image segmentation Biomedical imaging Electronic mail computer networks Pixel image resolution

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Length-limited variable-to-variable length codes for high-performance entropy coding

Length-limited variable-to-variable length codes for high-pe...

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Data Compression Conference (DCC)

作者： J. Senecal M. Duchaineau K.I. Joy Institute for Scientific Computing Research Lawrence Livemore National Laboratory USA Center for Image Processing and Integrated Computing Computer Science Department University of California Davis USA Center for Applied Scientific Computing Lawrence Livemore National Laboratory USA

Arithmetic coding achieves a superior coding rate when encoding a binary source, but its lack of speed makes it an inferior choice when true high-performance encoding is needed. This paper presents the practical implementation of fast entropy coders for binary messages utilizing only bit shifts and table lookups. To limit code table size the proposed code lengths is limited with a type of variable-to-variable (VV) length code created from source string merging. This is referred to as "merged codes". With merged codes it is possible to achieve a desired level of speed by adjusting the number of bits read from the source at each step. The most efficient merged codes yield a coder with a worst-case inefficiency of 0.4%, relative to the Shannon entropy. Using a hybrid Golomb-VV bin coder the compression ratio that is competitive with other state-of-the-art coders, at a superior throughput is achieved.

关键词： Entropy coding image coding Table lookup Throughput Computational modeling Scientific computing Laboratories Degradation Arithmetic image processing

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