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7th International Conference on image and Signal processing and their Applications, ISPA 2022

作者： Menasri, Wahiba Meddah, Karim University of Sciecnes and Technology Houari Boumediene Usthb Laboratory of Image Processing and Radiation Algiers Algeria University Yahya Fares of Medea Nouveau Pôle Urbain Faculty of Technology Medea Algeria Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontrealQC Canada Usthb Laboratory of Instrumentation Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9781665480420

Ultra High Definition Television (UHDTV) imposes extremely high throughput requirement on video encoders based on High Efficiency Video Coding (H.265/HEVC). HEVC adopt many advanced developed techniques in order to compress and decompress the video sequence for ensuring the real time requirements with keeping the quality. The decoded video is obtained according to the syntax elements language specified by the HEVC standard. Consequently, HEVC adopt 74 syntax elements at the low level which are divided into four partitions: including quad tree partitioning, intra and inter prediction, transform level, quantization and loop filtering. This work propose a profound study of HEVC low level syntax elements. The four partitions of syntax elements are defined, detailed and specified (role). After that, the encoding process and order of each syntax element is given by diagram blocks followed by an examples of encoding process syntax elements for 4x4 and 8x8 transform blocks (references blocks) by using Matlab ***, performance comparison of different benchmarks is given in order to highlights the decoding efficiency of HEVC compared it predecessor AVC (Advenced Video Coding). This work give the only detailed study that demonstrate the main functionality of the all intra syntax elements encoding process in CABAC HEVC. © 2022 IEEE.

关键词： MATLAB

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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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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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Multimodal Brain-computer Interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： Brain computer interface

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An Automatic Colorectal Polyps Detection Approach for Ct Colonography

An Automatic Colorectal Polyps Detection Approach for Ct Col...

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IEEE International Conference on image processing

作者： Mohamed Yousuf Islam Alkabbany Asem Ali Salwa Elshazley Albert Seow Gerald Dryden Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Kentucky Imaging Technologies Louisville KY School of Medicine University of Louisville Louisville KY

In this work, we propose an automatic colorectal polyps detection approach that consists of two cascade stages. In the first stage, a CNN model is trained to detect polyps in axial CT slices, The CNN model has been fed by the segmented colon wall CT slices instead of the original CT slices. Using the segmented images as an input to the CNN model has drastically improved the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, the second stage classifier is deployed to exploit the different views of the CT scans instead of the axial view only. So, the classifier is trained using the 2D images of axial views, i.e., the candidate polyps generated by the detector, as well as their corresponding 2D images of sagittal and coronal views. The experimental results of this approach were validated by 3 radiologists and the approach successfully identified polyps after the classification stage with an AUC ∼ 98.6%.

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Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction

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IEEE Journal of Biomedical and Health Informatics 2023年第6期29卷 3842-3852页

作者： Tingting Xu Yongyong Chen Haijin Zeng Guokai Zhang Jingyong Su School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Image Processing and Interpretation Imec research group Ghent University Ghent Belgium School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling $\bf k$ - $t$ space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images. (2) they focus on preserving global information only, while ignoring the local details reconstruction such as the spatial piece-wise smoothness and sharp boundaries. To overcome these obstacles, we suggest a novel low-rank tensor decomposition approach by integrating tensor Qatar Riyal (QR) decomposition, low-rank tensor nuclear norm, and asymmetric total variation to reconstruct dMRI, named TQRTV. Specifically, while preserving the tensor inherent structure by utilizing tensor nuclear norm minimization to approximate tensor rank, QR decomposition reduces the dimensions in the low-rank constraint term, thereby improving the reconstruction performance. TQRTV further exploits the asymmetric total variation regularizer to capture local details. Numerical experiments demonstrate that the proposed reconstruction approach is superior to the existing ones.

关键词： Tensors image reconstruction Matrix decomposition Magnetic resonance imaging Imaging Transforms Discrete Fourier transforms

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Accurate Colon Segmentation Using 2D Convolutional Neural Networks With 3D Contextual Information

Accurate Colon Segmentation Using 2D Convolutional Neural Ne...

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IEEE International Conference on image processing

作者： Samir Harb A. Elsayed M. Yousuf I. Alkabbany A. Ali S. Elshazley A. Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Higher Technological Institute 10th of Ramadan City Egypt Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Engineering Assiut University Assiut Egypt Kentucky Imaging Technologies Louisville KY

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

This study introduces an innovative framework designed specifically for accurate colon segmentation in abdomen CT scans, tackling the distinct challenges inherent to this task. Building upon well-established 2D segmentation models, our architecture adeptly incorporates 3D contextual information via a novel method that generates an attention map for a given slice by considering its neighboring slices in a sequence. Our approach accomplishes effective colon segmentation without requiring complex 3D convolutional neural networks (CNNs) or Long Short-Term Memory (LSTM) networks by combining 2D CNNs. Validated on a dataset of 98 CT scans from 49 patients, the architecture exhibits notable performance, successfully capturing nuanced details crucial for precise colon segmentation. The experiments encompass a thorough examination of model selection and cross-validation, providing valuable insights into the efficacy of our proposed approach. The outcomes underscore the potential for streamlined colon segmentation in medical imaging by judiciously integrating 2D and 3D information, employing solely 2D networks, and mitigating challenges associated with 3D networks. The code for model architecture is available at: https://***/Samir-Farag/***

关键词： image segmentation Solid modeling Three-dimensional displays Computed tomography Predictive models Colon Convolutional neural networks

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Multi-View Network for Colorectal Polyps Detection in CT Colonography

Multi-View Network for Colorectal Polyps Detection in CT Col...

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IEEE International Conference on image processing

作者： Mohamed Yousuf Samir Harb Islam Alkabbany Asem Ali Salwa Elshazley Aly Farag Computer Vision and Image Processing Laboratory (CVIP) University of Louisville Louisville KY Faculty of Engineering Ain Shams University Cairo Egypt Higher Technological Institute 10th of Ramadan City Egypt Faculty of Engineering Assiut University Assiut Egypt Kentucky Imaging Technologies Louisville KY

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Early diagnosis of colorectal polyps, before they turn into cancer, is one of the main keys to treatment. In this work, we propose a framework to help radiologists in reading CT scans and identifying candidate CT slices that have polyps. We propose a colorectal polyps detection approach which consists of two cascaded stages. In the first stage, a CNN-based model is trained and validated to detect polyps in axial CT slices. To narrow down the effective receptive field of the detector neurons, the colon regions are segmented and then fed into the network instead of the original CT slice. This drastically improves the detection and localization results, e.g., the mAP is increased by 36%. To reduce the false positives generated by the detector, in the second stage, we propose a multi-view network (MVN) that classifies polyp candidates. The proposed MVN classifier is trained using sagittal and coronal views corresponding to the detected axial views. The approach is tested in 50 CTC-annotated cases, and the experimental results confirm that after the classification stage, polyps can be detected with an AUC $\sim 95.27 \%$.

关键词： Location awareness Sensitivity Computed tomography Neurons Detectors Colonography Feature extraction

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A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

引用

3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Soumeya Ourabia Nadia Baaziz Youcef Smara Departement of Electronics University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Tipaza Algeria Department of Computer Science and Engineering Research Laboratory in Multimedia Information University of Quebec in Outaouais - UQO Gatineau Québec Canada Laboratory of Image Processing and Radiation Faculty of electrical engineering University of Sciences and Technology Houari Boumediene -USTHB Algiers Algeria

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods.

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