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conference applications of Digital image processing xLIV

作者： Castillo, D. Macas, J. Samaniego, R. Jimenez, Y. Diaz, P. Rodriguez-Alvarez, M. J. Lakshminarayanan, Vasudevan Univ Tecn Particular Loja Chem Dept Ap 11-01-608 Loja Ecuador Hosp UTPL Dept Radiol Loja Ecuador Univ Tecn Particular Loja Hlth Sci Dept Loja Ecuador Univ Politecn Valencia Inst Instrumentac Imagen Mol I3M CSIC Valencia Spain Univ Waterloo Sch Optometry & Vis Sci Theoret & Expt Epistemol Lab Waterloo ON Canada Univ Waterloo Dept Phys Elect & Comp Engn Waterloo ON Canada Univ Waterloo Dept Syst Design Engn Waterloo ON Canada

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Epilepsy is a chronic neurological disorder that causes unprovoked and recurrent seizures which according to WHO affects approximately 50 million people worldwide. Functional magnetic resonance images (MRI) help to identify certain affected areas of the brain, namely, the gliosis and hippocampal volume loss. These losses cause complex epilepsy, and is known as hippocampal sclerosis or Mesial Temporal Sclerosis (MTS). This work presents the development of a Computer Aided Diagnosis CAD system software package) that can be used to identify the characteristics and patterns of MTS from brain magnetic resonance images. The image processing techniques involve texture analysis, statistical features, evaluation of the 3D Region of interest (ROI), and threshold analysis. The software allows the automatic evaluation of the degeneration of hippocampal structures, hippocampal volume and signal intensity. We will describe and demonstrate the software (which can currently be accessed on GitHub). It is expected that this tool will be useful in new neurology/radiology specialists and can serve as a secondary diagnosis. However, it is necessary to validate the software system qualitatively and quantitatively in order to get more effectiveness and efficiency in a real-world clinical application.

关键词： brain MRI Mesial temporal sclerosis hippocampal volume epilepsy texture features 3D ROI texture analysis

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Tunable Precision Control for Approximate image Filtering in an In-Memory Architecture with Embedded Neurons

Tunable Precision Control for Approximate Image Filtering in...

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IEEE International conference on Computer-Aided Design

作者： Ayushi Dube Ankit Wagle Gian Singh Sarma Vrudhula School of Computing and Augmented Intelligence Arizona State University Tempe AZ USA

This paper presents a novel hardware-software co-design consisting of a processing in-Memory (PiM) architecture with embedded neural processing elements (NPE) that are highly reconfigurable. The PiM platform and proposed approximation strategies are employed for various image filtering applications while providing the user with fine-grain dynamic control over energy efficiency, precision, and throughput (EPT). The proposed co-design can change the Peak signal to Noise Ratio (PSNR, output quality metric for image filtering applications) from 25dB to 50dB (acceptable PSNR range for image filtering applications) without incurring any extra cost in terms of energy or latency. While switching from accurate to approximate mode of computation in the proposed co-design, the maximum improvement in energy efficiency and throughput is 2x. However, the gains in energy efficiency against a MAC-based PE array with the proposed memory platform are 3x-6x. The corresponding improvements in throughput are 2.26x-4.52x, respectively.

关键词： PSNR Costs Neurons Random access memory Computer architecture Switches Throughput

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Visual roughened sensing for private human pose recognition

Visual roughened sensing for private human pose recognition

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2021 International conference on signal image processing and Communication, ICSIPC 2021

作者： Liu, Jixin Han, Guang Sun, Ning Li, xiaofei Engineering Research Center of Wideband Wireless Communication Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China Hangzhou Joyware Limited Corporation Hangzhou310051 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

The rapid development of the Internet has made people increasingly dependent on networks for information transmission. Visual media such as digital images and videos have gradually become two of the most important forms of information exchange because they can be disseminated very quickly. However, images and videos often contain private data, such as corporate secrets and personal identity information and the harm caused by leakage of such data cannot be underestimated. Therefore, the security of images and videos has attracted widespread attention from the public and related researchers. At present, the processing of images and videos tends to include the following steps: sampling, compression, encryption, transmission, decryption, decompression, reconstruction, and intelligent processing. During these steps, images and videos are encrypted and decrypted by the sender and the receiver respectively to avoid potential leakage of private data by interception during online transmission. This processing mode is mainly aimed at preventing information-leakage problems during transmission, but it ignores the risks caused by the intelligent application of images and videos after their reconstruction. For the contradiction between existing data processing methods and actual social needs, a novel visual roughened sensing is proposed for typical intelligent applications such as private human pose recognition. © 2021 SPIE.

关键词： image reconstruction

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16th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th International conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

16th International Conference on Intelligent Information Hid...

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16th International conference on Intelligent Information Hiding and Multimedia signal processing, IIH-MSP 2020 in conjunction with the 13th International conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

ISBN: (纸本)9789813367562

The proceedings contain 126 papers. The special focus in this conference is on Intelligent Information Hiding and Multimedia signal processing. The topics include: Thin Point Light Source Display;dynamic Token Distribution Model for Privacy Protection of Mobile Users;cascading Fault Prevention of Power Grid Based on Key Power Generation Nodes;optimal Power Generation Output Considering Cascading Failure;A Survey of Common IOT Communication Protocols and IOT Smart-x applications of 5G Cellular;study of Thermal Power Plant’s Intelligent Fire Detection and Suppression System Via Wireless Sensor Network and Carbon Capture and Storage Technology;game-Theoretic Decision-Making Analysis on Antivirus;A New JPEG Encryption Scheme Using Adaptive Block Size;study of Smart Decorating Machine on Cake Patten;CNN Character Recognition Model for 3D Integral image Character;Study of Advanced Low-Cost Smart Prepaid Electricity Meter Using Arduino and GSM;study of Integrating the Data Fusion Method for Reducing and Preventing Road Accidents Occur at Blackspots Places in Third World Countries;study of 2D SubMarine Tracking with Complete Worked Out Example Based on Kalman Filter;Retina Macular Edema and Age-Related Macular Degeneration Feature Recognition Method Based on the OCT images;Gene Expression PPI Network Clustering Analysis Between Endometrial Cancer and Ovarian Cancer;Automatic Identification and Classification Method for Diabetic Retinopathy FFA image processing;emotional Expression Analysis Based on Fine-Grained Emotion Quantification Model Via Social Media;Fuzhou PM2.5 Prediction and Related Factors Analysis;an Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir;prediction of Hypertension Using Deep Autoencoder-Based Feature Representation.

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16th International Conference on Intelligent Information Hid...

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ISBN: (纸本)9789813364196

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Segmentation of Teeth Region via Machine Learning in Panoramic x-Ray Dental images 28

Segmentation of Teeth Region via Machine Learning in Panoram...

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28th signal processing and Communications applications conference (SIU)

作者： Guven, Ali Yetik, Imam Samil Culhaoglu, Ahmet Orhan, Kaan Kilicarslan, Mehmet Kilicarslan TOBB Ekon & Teknol Univ Elekt Elekt Muhendisligi Bolumu Ankara Turkey Kirikkale Univ Dis Hekimligi Fak Kirikkale Turkey Ankara Univ Dis Hekimligi Fak Ankara Turkey

ISBN: (纸本)9781728172064

Segmentation of teeth region from the dental panoramic x-Ray images is an important task in determining various diseases. The main goal of this article is to be able to automatically segment the region of teeth in panoramic x-ray images. First, the center point of the teeth area in the images was determined automatically. Then, a feature set was developed including intensity values of pixels, x-coordinate relative to this center point, y-coordinate relative to this point, and the pixel values obtained by subtraction of maximum and minimum values in 3x3 window. CatBoost algorithm was used for machine learning. When creating the machine learning model, k-fold cross validation of training data set and grid search optimization of hyper parameters, were applied to avoid over fitting of data set. The results were analyzed using the learning curve, F1, accuracy, recall, and precision scores.

关键词： dental panoramic x-Ray images machine learning image processing image segmentation teeth segmentation

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Study of VGG-19 Depth in Transfer Learning for COVID-19 x-Ray image Classification 11th

Study of VGG-19 Depth in Transfer Learning for COVID-19 X-Ra...

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11th International conference on Robotics, Vision, signal processing and Power applications, RoViSP 2021

作者： Hamad, Qusay Shihab Samma, Hussein Suandi, Shahrel Azmin Saleh, Junita Mohamad School of Electrical and Electronic Engineering Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal14300 Malaysia Johor Johor Bahru81310 Malaysia Baghdad Iraq

ISBN: (纸本)9789811681288

Modern-era largely depends on Deep Learning (DL) in a lot of applications. Medical images Diagnosis is one of the important fields nowadays because it is related to human life. But this DL requires large datasets as well as powerful computing resources. At the beginning of 2020, the world faced a new pandemic called COVID-19. Since it is new, shortage of reliable datasets of a running pandemic is a common phenomenon. One of the best solutions to mitigate this shortage is taking advantage of Deep Transfer Learning (DTL). DTL would be useful because it learns from one task and could work on another task with a smaller amount of dataset. This paper aims to examine the application of the transferred VGG-19 to solve the problem of COVID-19 detection from a chest x-ray. Different scenarios of the VGG-19 have been examined, including shallow model, medium model, and deep model. The main advantages of this work are two folds: COVID-19 patient can be detected with a small number of data sets, and the complexity of VGG-19 can be reduced by reducing the number of layers, which consequently reduces the training time. To assess the performance of these architectures, 2159 chest x-ray images were employed. Reported results indicated that the best recognition rate was achieved from a shallow model with 95% accuracy while the medium model and deep model obtained 94% and 75%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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A positioning method of ablation needles in x-ray CT images 6

A positioning method of ablation needles in X-ray CT images

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6th International conference on Communication, image and signal processings, CCISP 2021

作者： Sun, Lei Chen, Ming Li, Gang Han, Lixin Han, Jingqi Gu, Hao Zhang, Min College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China Department of Interventional Radiology the Affiliated Hospital of Qingdao University Qingdao China

ISBN: (纸本)9781665432795

CT image-guided interventional therapy has been widely used in clinical application, especially for cancer treatments. However, metal artifacts often appear in CT imaging of the metal ablation needles. These artifacts seriously affect the doctor's diagnosis of the ablation needle positioning. We propose an accurate positioning method which can correct the artifacts and detect the geometric information of ablation needles. Firstly, the layered technology of the multi-energy projection data is given to eliminate metal artifacts;then the corrected CT images are preprocessed for positioning;finally, the "pinpoint" positioning of the needles is realized by DBSCAN (Density-Based Spatial Clustering of applications with Noise) clustering. The experimental results show that the proposed method can provide fast and accurate positioning for the ablation needle. © 2021 IEEE.

关键词： Ablation

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A Hybrid Cross-Scale Transformer Architecture for Robust Medical image Segmentation

A Hybrid Cross-Scale Transformer Architecture for Robust Med...

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2022 IEEE International conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ning, Yang Zhang, Shouyi Zhong, Wei Liu, Peide Zhang, Caiming Shandong Jianzhu University School of Computer Science and Technology Jinan China The University of Edinburgh School of Informatics Edinburgh United Kingdom Guangdong Police College Department of Network Information Security Guangzhou China Shandong University of Finance and Economics School of Management Science and Engineering Jinan China Shandong University School of Software Jinan China

ISBN: (纸本)9781665468190

Transformer architecture has emerged to be successful in many natural language processing tasks. However, its applications to clinical practice remain largely unexplored. In this study, we propose a Robust Cross-Scale Hybrid Transformer (RCSHT) architecture for medical image segmentation, which can effectively enhance the multi-scale feature representations while integrating local features with global dependencies. Specifically, we propose two new modules based on the self-attention mechanism: PSCM and PCCM, which perform matrix low-rank transformation on cross-scale images in spatial and channel space, respectively, to effectively enhance the discriminant ability of long-range dependencies of multi-scale images and effectively reduce the computational complexity. Meanwhile, we use spatial positional encoding to perform spatial regularization on the two attention modules, so that the model has more opportunities to obtain valuable spatial details and further enhance the spatial retention ability. Besides, by fusing outputs of the two modules, the robustness and discriminant ability of the feature representations are further improved. Note that our hybrid architecture allows transformers to be initialized as convolutional networks without pre-training. Extensive experiments conducted on our newly proposed dataset have demonstrated our RCSHT notably outperforms the state-of-the-art methods by a large margin, holding the promise to generalize well on other medical image segmentation. © 2022 IEEE.

关键词： signal encoding

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Fractional Spectral Graph wavelets and Their applications

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MATHEMATICAL PROBLEMS IN ENGINEERING 2020年第1期2020卷

作者： Wu, Jiasong Wu, Fuzhi Yang, Qihan Zhang, Yan Liu, xilin Kong, Youyong Senhadji, Lotfi Shu, Huazhong Southeast Univ Lab Image Sci & Technol LIST Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 210096 Peoples R China Univ Rennes INSERM LTSI UMR 1099 F-35042 Rennes France Univ Rennes Ctr Rech Informat Biomed Sino Francais CRIBs INSERM Rennes France Southeast Univ Nanjing Peoples R China Taiyuan Univ Technol Coll Data Sci Taiyuan 030024 Peoples R China

One of the key challenges in the area of signal processing on graphs is to design transforms and dictionary methods to identify and exploit structure in signals on weighted graphs. In this paper, we first generalize graph Fourier transform (GFT) to spectral graph fractional Fourier transform (SGFRFT), which is then used to define a novel transform named spectral graph fractional wavelet transform (SGFRWT), which is a generalized and extended version of spectral graph wavelet transform (SGWT). A fast algorithm for SGFRWT is also derived and implemented based on Fourier series approximation. Some potential applications of SGFRWT are also presented.

关键词： Wavelet transforms

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