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2nd International Workshop on Machine Learning for Medical image reconstruction, MLMIR 2019 held in Conjunction with 22nd International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

2nd International Workshop on Machine Learning for Medical I...

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2nd International Workshop on Machine Learning for Medical image reconstruction, MLMIR 2019 held in Conjunction with 22nd International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

ISBN: (纸本)9783030338428

The proceedings contain 24 papers. The special focus in this conference is on Machine Learning for Medical image reconstruction. The topics include: Measuring CT reconstruction Quality with Deep Convolutional Neural Networks;deep Learning Based Metal Inpainting in the Projection Domain: Initial Results;flexible Conditional image Generation of Missing data with Learned Mental Maps;Spatiotemporal PET reconstruction Using ML-EM with Learned Diffeomorphic Deformation;stain Style Transfer Using Transitive Adversarial Networks;Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer;Deep Learning Based Approach to Quantification of PET Tracer Uptake in Small Tumors;Task-GAN: Improving Generative Adversarial Network for image reconstruction;gamma Source Location Learning from Synthetic Multi-pinhole Collimator data;Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging;neural Denoising of Ultra-low Dose Mammography;image reconstruction in a Manifold of image Patches: Application to Whole-Fetus Ultrasound Imaging;image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy;TPSDicyc: Improved Deformation Invariant Cross-domain Medical image Synthesis;PredictUS: A Method to Extend the Resolution-Precision Trade-Off in Quantitative Ultrasound image reconstruction;fast Dynamic Perfusion and Angiography reconstruction Using an End-to-End 3D Convolutional Neural Network;APIR-Net: Autocalibrated Parallel Imaging reconstruction Using a Neural Network;Accelerated MRI reconstruction with Dual-Domain Generative Adversarial Network;Deep Learning for Low-Field to High-Field MR: image Quality Transfer with Probabilistic Decimation Simulator;joint Multi-anatomy Training of a Variational Network for reconstruction of Accelerated Magnetic Resonance image Acquisitions;modeling and Analysis Brain Development via Discriminative Dictionary Learning;Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slic

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Research on image Analysis and Processing Technology Based on Big data Technology 2

Research on Image Analysis and Processing Technology Based o...

引用

2nd IEEE International conference on Electrical Engineering, Big data and Algorithms, EEBDA 2023

作者： Hu, Yahui Xi, Qiushi Xiao, Lei Fang, Qiqing Hu, Yamin Early Warning Academy Wuhan China

ISBN: (纸本)9781665462532

With the increase of information, the traditional image processing technology still has a big shortage in image quality and processing speed. Therefore, how to improve the image quality and processing speed is a research hotspot. To solve the above problems, we adopt the method of combining the generative countermeasure network with the super-resolution reconstruction, and use the open data set to train the model. Through the reasonable selection and optimization of the image processing algorithm, we can achieve the goal of improving the image super-resolution reconstruction effect. In order to improve the slow image processing speed, we set up Spark cluster to integrate computing resources. The experimental results show that the method based on the generation of countermeasure network has a good effect for high resolution image reconstruction, and this method can expand the resolution of low resolution image by four times;The cluster based on the Spark platform shows better performance advantages in processing a large number of image data. When the hardware indicators of the cluster environment and the stand-alone environment are consistent, the cluster based on the Spark platform can increase the running speed of the program by about 10%. When the data volume reaches the tera-byte level or even the petabyte level, the operation speed of the system can still be improved by increasing the overall running memory of the cluster. © 2023 IEEE.

关键词： Big data

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image Compression and Stable reconstruction Based on Multi-Level Hybrid Feature Guidance 2

Image Compression and Stable Reconstruction Based on Multi-L...

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2nd International conference on Machine Learning, Cloud Computing, and Intelligent Mining, MLCCIM 2023

作者： Zeng, Heng Gong, Nanxu College of Software Engineering Sichuan University Chengdu China School of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9798350328530

image compression is a technique that reduces storage space and transmission bandwidth requirements by reducing the amount of image data, aiming to improve storage and transmission efficiency while maintaining acceptable image quality. In recent years, learning-based image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression standards. Nevertheless, existing approaches often struggle to simultaneously achieve high compression rates and high-quality reconstruction. To tackle this issue, we present an image compression and stable reconstruction method based on multi-level hybrid feature guidance. Firstly, a hybrid attention module that combines spatial, channel, and shuffle attention is designed to improve the model's ability to capture local features of the image. Secondly, we utilize boundary prediction and frequency domain feature prediction auxiliary branches to guide the network for image compression and reconstruction. Moreover, we propose an image compression and reconstruction algorithm framework based on multi-level feature guidance. Based on experimental results, our method surpasses existing approaches in achieving superior rate-distortion performance. These research results are of great significance to the further development of image compression and reconstruction technology. © 2023 IEEE.

关键词： image compression

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Finite element method based neural network technique for image reconstruction in electrical impedance imaging

Finite element method based neural network technique for ima...

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Proceedings of the 1998 2nd International conference on Biomedical Engineering Days

作者： Kilic, Beyhan Korurek, Mehmet Yildiz Technical Univ Istanbul Turkey

image reconstruction in Electrical Impedance Tomography (EIT) is a nonlinear inverse problem and typically ill-conditioned. A direct consequence of the ill-posedness is high sensitivity errors in measurements. In addition several assumptions made to reduce computational complexity are, in fact, rough approximation. This factors contribute to a limited spatial resolution and result in low accuracy in EIT images. Accordingly to improve electrical impedance images it is necessary to evaluate collected data with a new approach. This paper presents a reconstruction algorithm based neural network technique which calculates conductivity changes directly from finite-element simulations of forward problem. The advantages of this method are it's speed of image reconstruction it's conceptual simplicity, and ease of implementation.

关键词： Medical imaging

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A New Radiation Source Identification Algorithm 2

A New Radiation Source Identification Algorithm

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2nd International conference on Big data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Zhao, Jing Yang, Panpan Zhang, Haihong Lin, Jinchao Unit 92020 PLA Beijing Peoples R China Unit 91033 PLA Beijing Peoples R China

ISBN: (纸本)9781665427098

In order to solve the deficiencies of the existing incomplete information system radiation source identification algorithm, a new emitter recognition method to incomplete information system based on rough set theory is presented. This recognition method does not change emitters' original information, reduces knowledge and finds decision rules directly from incomplete decision table. Examples of recognizing the radar emitter is selected to demonstrate this new method. Variable precision rough set model introduced a parameter beta, which can be used to solve the problem of classification while there is uncertain relation among attributions describing objects. The domain of parameter beta is studied. A new theorem about the domain of parameter beta is presented and the corresponding proof is completed. A new method of finding decision rules is presented from a new point of view. This method is according to dependent degree of decision attributes on condition attributes. What follows that decision rules proposed is more straightforward. Example of recognizing the radar emitter is given, and the experimental results prove the effectiveness of the new method.

关键词： Variable Precision Rough Set Model (VPRSM) emitter recognition method incomplete information discretization

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Evaluation of Surrogate data Quality in Sinogram-Based CT Metal-Artifact Reduction

Evaluation of Surrogate Data Quality in Sinogram-Based CT Me...

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conference on image reconstruction from incomplete data V

作者： Oehler, May Kratz, Baerbel Knopp, Tobias Mueller, Jan Buzug, Thorsten M. Univ Lubeck Inst Med Engn D-23538 Lubeck Germany

ISBN: (纸本)9780819472960

In this work different surrogate data strategies to reduce metal artifacts in reconstructed CT images are tested. Inconsistent sinogram projection data caused by e.g. beam hardening are the origin of metal artifacts in the reconstructed images. The goal of this work is to replace this inconsistent projection data by artificially generated data. Therefore, here, two I D interpolation strategies, a directional interpolation based upon the sinogram 'flow' and a I D interpolation by means of the non-equispaced fast Fourier transform are compared to a fully 2D method based upon the idea of image inpainting. Due to the fact that the artificially generated data never perfectly fit the gap inside the projection data caused by the inconsistencies, those repaired sinogram data are reconstructed using a weighted Maximum Likelihood Expectation Maximization algorithm called lambda-MLEM algorithm. In this way, the artificially generated data, still contaminated with residual inconsistencies, are weighted less during reconstruction.

关键词： Metal artifact reduction MLEM reconstruction image inpainting non-equispaced fast Fourier transformation computed tomography surrogate data strategy

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Deep Learning based image Enhancing Environment with Noise Suppression 2

Deep Learning based Image Enhancing Environment with Noise S...

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2nd International conference on Electronics and Renewable Systems, ICEARS 2023

作者： Sivanantham Reddy, Basireddy Satish Kumar Saignaneswar, J. Balaji, Kalahasti Vivek Reddy, K. Siva Reddy, Kusam Lokesh Svec Computer Science and Systems Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350346640

A deep learning approach will be used to recover ancient pictures that have suffered significant damage. Unlike typical reconstruction processes that are easily handled by supervised learning methods, real-world picture degradation seems to be complex, and the system is unable to generalize due to domain differences between synthetic pictures and actual old pictures. Therefore, using huge amounts of synthetic image pairs combined with real photos, Therefore, using huge amounts of synthetic picture pairs combined with real photos, A unique triplet domain translation network. Two variational autoencoders (VAEs) have been trained to create latent spaces from both fresh and old images, respectively. The translation between two regions is thenmanaged to learn using artificially paired data. This translation normalizes well to actual photographs as the domain gap is filled in the compact latent space. The translation between these two various latent regions has been taught using artificially paired data. This translation normalizes well to images found in the real world because the compact latent space is filled with the domain gap. A global division with an incomplete nonlocal block will target structural issues like cuts and bruises and a local division attacking unstructured defects like unwanted noise and poor contrast to handle the various degradations mixed throughout an old photograph. The latent space fusion of two branches increases the ability to correct numerous flaws in old images. Convolutional neural networks (CNNs) outperform multiple-layer sequenced neural network models at identifying distinct marks, forms, and patterns in images, making them the most efficient method for processing data. The filters are applied by CNN to every pixel in the image. When it comes to visual quality, the suggested method for repairing old photographs performs better than cutting-edge techniques. © 2023 IEEE.

关键词： image reconstruction

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data management and modeling for reconstructing 3D finite elements of lungs

Data management and modeling for reconstructing 3D finite el...

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2nd International conference on Pervasice Computing and Applications

作者： Li, Tao Yao, Jin Wu, Dake Yang, Jiawei Sichuan Canc Hosp Dept Radiotherapy Chengdu Peoples R China Sichuan Univ Sch Mfg Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9781424409709

For biomechanical analysis, reconstruction of 3D finite-element model based on sequential medical images is essential. However, the burdensome preprocessing tasks as image data processing and meshing for such process have turned out to be the bottleneck of finite-element analysis. In particular, lung has extremely complicated histological components resulting in rather irregular shape and diversity of stresses, so data related to the medical images for establishment lung model are quite complicated. Based on data necessary for establishing 3D finite-element model, this paper presents the data structure and types required for processing based on CT medical images and analyses the methods for data acquisition and processing. Also, a data processing method based on the idea of sub-divisional modeling is introduced, which is in combination with MSC. Marc software to have realized the meshing and established 3D finite-element model of lung. The morphological characteristics of the model basically embody the physical characteristic form of lungs, and satisfy the requirements for biomechanical studies and analysis on the law of lung motion. This is quite significant for data acquisition and management for the processing of medical sequential images and finite-element modeling in addition to the significances on the further development and practical guidance of other similar reconstruction of 3D finite-element human tissues based on medical images (CT and MRI etc).

关键词： lung CT image finite elements biomechanical model data structure meshing

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GCD based Blind Super-Resolution for Remote Sensing Applications 2

GCD based Blind Super-Resolution for Remote Sensing Applicat...

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2nd International conference on Energy, Power and Environment - Towards Smart Technology (ICEPE)

作者： Sharma, Neerav Dash, Prajna Parimita Saxena, Priyank Birla Inst Technol Dept Elecntron & Commun Engn Ranchi Bihar India

ISBN: (纸本)9781538647691

The importance of remote sensing imageries is growing day by day. Extraction of fine details of desired regions worth for further processing and decision making. Usually the data bases of remote sensing imageries are very huge that overburden the processor. Super-Resolution overcomes this problem and yields a high-quality output in less time consumption. This paper aims to give a brief idea about one of the approaches of super-resolution known as blind super-resolution reconstruction approach. In this approach, Greatest Common Divisor (GCD) algorithm is embedded into the blind reconstruction technique. The HR images obtained from this method is compared with the interpolated images. The results shows the efficacy of the proposed method. The paper tries to overcome the limitations of the super resolution approach and a conclusive discussion of the whole method has been discussed.

关键词： Remote sensing Satellite image Super resolution GCD Blind reconstruction

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Theoretical uncertainty evaluation of stereo reconstruction

Theoretical uncertainty evaluation of stereo reconstruction

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2nd International conference on Bioinformatics and Biomedical Engineering, iCBBE 2008

作者： Chen, Jiechun Ding, Zhenliang Yuan, Feng Department of Automatic Measurement and Control Harbin Institute of Technology Harbin China

ISBN: (纸本)9781424417483

This paper deals with the problem of evaluating the uncertainties associated with linear camera model based stereo reconstruction using the law of propagation of uncertainty. The procedure of stereo reconstruction involves two consecutive stages: calibrating camera models and reconstructing a 3D point from its image projections. The output quantities of the first stage, the parameters of camera models, constitute a part of the input quantities of the second stage. The analytical expressions for uncertainty propagation during reconstructing a 3D point are proposed, and the results of an experiment with synthetic data are also presented. The experiment results show that there is at least one significant digit in the evaluated uncertainties associated with the reconstructed coordinates of a 3D point. © 2008 IEEE.

关键词： image reconstruction

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