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4th International Conference on Pattern Recognition and image analysis, IPRIA 2019

作者： Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Laboratory Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728116211

Short-term load forecasting (STLF) is one of the main challenging problems in management of power systems. By emerging the new technologies and development of smart grids, STLF becomes a necessity. A STLF framework is proposed in this work which uses the long short-term memory (LSTM) network for forecasting. In the proposed framework, a feature vector containing the lagged load values is considered for each point of the load time series where this feature vector can be given to the LSTM network in three ways: 1) the original feature vector (LSTM), 2) the coefficients of discrete wavelet transform of the feature vector (DWT-LSTM), and 3) the stacked vector of wavelet coefficients and original load values (DWT-stack-LSTM). In the proposed DWT-stack-LSTM method, both of original values of load and decomposition values of load in different frequencies and scales are given to LSTM. Thus, LSTM is trained to learn more about load variables and so, provides more accurate forecasting results. © 2019 IEEE.

关键词： Discrete wavelet transforms

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Correlation based Convolutional Recurrent Network for Load Forecasting

Correlation based Convolutional Recurrent Network for Load F...

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Iranian Conference on electrical engineering (ICEE)

作者： Hosein Eskandari Maryam Imani Mohsen Parsa Moghadam Image processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

The safe and economical operation of a power grid is not possible without knowing the future load. For this reason, the first step in terms of productivity and proper management of a system will be to predict the electric load in the future. In this paper, the features that exist in the history of electric load consumption are examined and they are used as a guide for designing the proposed method. We try to predict short-term electric load by extracting the characteristics of the electric load history using deep neural networks. Long Short Term Memory (LSTM), are able to hold short and long-term memory for extracting relationships between the load values from time series. On the other hand, convolution neural networks are capable of automatically learning features and can directly generate a vector for prediction. The Correlation based Convolution Recurrent Network (CCRN) which is proposed in this paper calculate the autocorrelation coefficients of the load series and obtain an quasi-periodic of hourly loads with the highest correlations. Then, by using this quasi-periodic, the load series is converted into a two-dimensional matrix (image) and the two-dimensional convolutional neural networks is used to extract the load features. Finally, the load time sequence information is extracted using the LSTM network. Experimental results on both the Toronto and ISO-NE datasets show reduced prediction error as well as reduced training time compared with LSTM networks.

关键词： Correlation Training Neural networks Feature extraction Kernel Load forecasting Forecasting

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Multispectral Pansharpening by Extracting Local Coefficients Based on WT Concept and MTF of Multispectral Sensors

Multispectral Pansharpening by Extracting Local Coefficients...

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Iranian Conference on electrical engineering (ICEE)

作者： Seyyedeh Zahra Azarnivar Hassan Ghassemian Science and Research Branch Islamic Azad University Tehran Iran Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728172965

ISBN: (纸本)9781728172972

In order to increase the spatial resolution of the multispectral images, the fusion process is used that the high frequency spatial information of the panchromatic image is added to the multispectral image. In this paper, first, the panchromatic image of the histogram matching each multispectral image band and each multispectral image band interpolated to the panchromatic image scale was divided. A significant and soluble linear equation was obtained between the high-frequency spatial information of the multispectral image and the panchromatic image, and this model was applied to each local window. The scale invariance assumption was used to obtain the coefficients of fusion and for this purpose, the concept of wavelet transform was used, with its low pass filter frequency response being identical to the MTF multispectral image sensor. The proposed method was applied to two worldview2 and IKONOS datasets. The results were competitive with some popular fusion algorithms.

关键词： Spatial resolution Sensors image sensors Visualization Low-pass filters Nonlinear distortion Sensor fusion

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Systolic Murmurs Diagnosis Improvement by Feature Fusion and Decision Fusion

Systolic Murmurs Diagnosis Improvement by Feature Fusion and...

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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： Somayeh Akbari Hassan Ghassemian Zahra Akbari Image processing and Information Analysis Lab. Faculty of Electrical and Computer Eng. Tarbiat Modares University Tehran Iran

Acoustic sound generated by the heart mechanical activity, can provide useful information about the condition of heart valves. The heart sound auscultation is the fundamental tool in the evaluation of the cardiovascular system. The advantage of this method is fast, inexpensive and noninvasive. Due to human auscultatory limitation and non-stationary characteristics of phonocardiogram signals (PCG), diagnosis based on sounds that are heard via a stethoscope is difficult skill, therefor it requires a lot of practice. This study has proposed a biomedical automatic system for classification of PCG signals, which, recorded by a digital stethoscope. In order to extract various characteristics of PCG signals, the power spectrum estimation, wavelet transform (WT) and Mel frequency Cepstrum coefficients (MFCC) have been used in feature extraction step. Features are given to four classifiers: support vector machine (SVM), k-nearest neighbor (k-NN), multilayer perceptron (MLP) and maximum likelihood (ML). The majority voting combination rule is utilized for fusion of different classifiers. The proposed method has been examined on dataset of 90 PCG records containing healthy and three types of cardiac valve diseases (pulmonary stenosis (PS), Atrial Septal Defect (ASD) and Ventricular Septal Defect (VSD)). The experimental results demonstrate that the classifier fusion rule significantly increases the diagnostic accuracy of abnormal PCG. Our proposed method can be used for online classification of PCG in intelligent diagnosis systems.

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A Model-Based Method for Pan-Sharpening of Multi-Spectral images using Sparse Representation

A Model-Based Method for Pan-Sharpening of Multi-Spectral Im...

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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： Mohammad Khateri Hassan Ghassemian Fardin Mirzapour Faculty of Electrical and Computer Engineering Image Processing and Information Analysis Lab. Tarbiat Modares University Tehran Iran Faculty of Electrical Engineering Sadra Institute of Higher Education Tehran Iran

Pan-sharpening (PS) is fusion of the low-resolution multi-spectral (LRM) image with the corresponding high resolution panchromatic (HRP) one, which aims to reach the high-resolution multi-spectral (HRM) image. Due to the importance of designing well-adapted dictionaries for the pansharpening problem as the fundamental challenge in the pan-sharpening methods utilizing sparse representation, we present a novel strategy to tackle this issue. Our method takes the image formation model into account in a patch-based strategy and exploits local spectral-spatial information. To make a fair tradeoff between spectral and spatial information of each patch, some local parameters are considered and estimated adaptively. In addition, the energy ratio between LRM/LRP patches is considered to locally reconstruct the same ratio for the corresponding HRM/HRP patches. This model-based pan-sharpening strategy led to a closed-form solution, which results in precise HRM dictionary atoms. To obtain the unknown HRM image, after designing the HRM dictionary from input Pan and multi-spectral images, sparse representation over LRM and HRM dictionaries will be conducted in the sparse coding stage. The proposed method has been applied to two different datasets collected by WorldView-3 and GeoEye-1, and then compared with some popular and state-of-the-art methods, qualitatively and quantitatively.

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Medical image Fusion based Sparse Decomposition and PDE of images

Medical Image Fusion based Sparse Decomposition and PDE of I...

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International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Fahim Shabanzade Hassan Ghassemian Mohammad Mahdi Sayadi Image Processing and Information Analysis Lab Faculty of Electrical and Computer Eng Tarbiat Modares University Tehran Iran

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

Medical image fusion is used for increasing the quality of interpretation and diagnosis in medical applications. In this paper, a new fusion technique is presented. The method is based on Partial Differential Equation (PDE) and Sparse Decomposition (SD), to fuse MRI and PET images. The source images decomposed into low and high-frequency components and each component segmented into some overlapped patches. In the first step, a set of PDE filters is applied to the MRI image, which produces a multi-scale representation of MRI image. The filters only differ in smoothing characteristics, which are controlled by two distinct parameters. In the second step, it learns the multiple primary dictionaries by using the multi-scale representation of the MRI image which owns the main features of the image. Finally, it forms the dictionary from these primary dictionaries which obtained from the previous step and use it for highfrequency patch estimation. For low-frequency patches, we use a weighted averaging approach. The experimental results demonstrate the priority of the proposed method, in terms of both visual quality and quantitatively assessment.

关键词： Dictionaries Magnetic resonance imaging Transforms image fusion Filtering theory information filters

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QUBIQ: Uncertainty Quantification for Biomedical image Segmentation Challenge

arXiv

引用

arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Classification of Panchromatic images Using Ripplet Transform and LBP Methods

Classification of Panchromatic Images Using Ripplet Transfor...

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International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Fatemeh Khalili Hassan Ghassemian Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Science and research University Tehran Iran Image Processing and Information Analysis Lab. Tarbiat Modares University Tehran Iran

ISBN: (纸本)9781728111957;9781728111940

Ripplet transform is one of effective methods in texture feature extraction. image classification is done in two steps: image feature extraction and automatic classification of these features. In the feature extraction step, rippletI, rippletII, curvelet and ridgelet transforms were used. These transforms yield appropriate results in identifying borders and edges of the figures. Local binary pattern (LBP) method is a simple got accurate method for identifying index class distribution. Hence, using staking method (combine ripplet transform and LBP methods) results in higher number of features vectors and improves the classification accuracy as much as 5%. Support vector machine (SVM) classify is used in classification step. Experimental results has been performed two databases (south Tehran and brotza).

关键词： Feature extraction Support vector machines Histograms Databases Wavelet transforms Two dimensional displays

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Dispensed transformer network for unsupervised domain adaptation

arXiv

引用

arXiv 2021年

作者： Li, Yunxiang Li, Jingxiong Dan, Ruilong Wang, Shuai Jin, Kai Zeng, Guodong Wang, Jun Pan, Xiangji Zhang, Qianni Zhou, Huiyu Jin, Qun Wang, Li Wang, Yaqi College of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Artificial Intelligence and Biomedical Image Analysis Lab Westlake University Hangzhou China School of Mechanical Electrical and Information Engineering Shandong University Weihai China Department of Ophthalmology Hospital of Zhejiang University Hangzhou China sitem Center for Translational Medicine and Biomedical Entrepreneurship University of Bern Bern Switzerland School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom Department of Human Informatics and Cognitive Sciences Faculty of Human Sciences Waseda University Tokyo Japan Department of Radiology and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill United States College of Media Engineering Communication University of Zhejiang Hangzhou China

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. However, it is costly to perform data annotation that provides ground truth lab.ls for training the supervised algorithms, and the high variance of data that comes from different domains tends to severely degrade system performance over cross-site or cross-modality datasets. To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper. Our novel DTNet contains three modules. First, a dispensed residual transformer block is designed, which realizes global attention by dispensed interleaving operation and deals with the excessive computational cost and GPU memory usage of the Transformer. Second, a multi-scale consistency regularization is proposed to alleviate the loss of details in the low-resolution output for better feature alignment. Finally, a feature ranking discriminator is introduced to automatically assign different weights to domain-gap features to lessen the feature distribution distance, reducing the performance shift of two domains. The proposed method is evaluated on large fluorescein angiography (FA) retinal nonperfusion (RNP) cross-site dataset with 676 images and a wide used cross-modality dataset from the MM-WHS challenge. Extensive results demonstrate that our proposed network achieves the best performance in comparison with several state-of-the-art techniques. © 2021, CC BY.

关键词： Supervised learning

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