检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Zhang, Jiawei Li, Chen Rahaman, Md Mamunur Yao, Yudong Ma, Pingli Zhang, Jinghua Zhao, Xin Jiang, Tao Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology Hobo-kenNJ07030 United States School of Resources and Civil Engineering Northeastern University Shenyang110004 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Institute of Medical Informatics University of Luebeck Luebeck23538 Germany

Microorganisms such as bacteria and fungi play essential roles in many application fields, like biotechnique, medical technique and industrial domain. Microorganism counting techniques are crucial in microorganism analysis, helping biologists and related researchers quantitatively analyze the microorganisms and calculate their characteristics, such as biomass concentration and biological activity. However, traditional microorganism manual counting methods, such as plate counting method, hemocytometry and turbidimetry, are time-consuming, subjective and need complex operations, which are difficult to be applied in large-scale applications. In order to improve this situation, image analysis is applied for microorganism counting since the 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. image analysis-based microorganism counting methods are efficient comparing with traditional plate counting methods. In this article, we have studied the development of microorganism counting methods using digital image analysis. Firstly, the microorganisms are grouped as bacteria and other microorganisms. Then, the related articles are summarized based on image segmentation methods. Each part of the article is reviewed by methodologies. Moreover, commonly used image processing methods for microorganism counting are summarized and analyzed to find common technological points. More than 144 papers are outlined in this article. In conclusion, this paper provides new ideas for the future development trend of microorganism counting, and provides systematic suggestions for implementing integrated microorganism counting systems in the future. Researchers in other fields can refer to the techniques analyzed in this paper. © 2021, CC0.

关键词： image segmentation

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EMDS-5: Environmental Microorganism image Dataset Fifth Version for Multiple image analysis Tasks

arXiv

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arXiv 2021年

作者： Li, Zihan Li, Chen Yao, Yudong Zhang, Jinghua Rahaman, Md Mamunur Xu, Hao Kulwa, Frank Lu, Bolin Zhu, Xuemin Jiang, Tao Microscopic Image and Medical Image Analysis Group MBIE College Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Biomedical Engineering Huazhong University of Science and Technology Wuhan430074 China Whiting School of Engineering Johns Hopkins University 500 W University Parkway MD21210 United States School of Control Engineering Chengdu University of Information Technology Chengdu610225 China

Environmental Microorganism Data Set Fifth Version (EMDS-5) is a microscopic image dataset including original Environmental Microorganism (EM) images and two sets of Ground Truth (GT) images. The GT image sets include a single-object GT image set and a multi-object GT image set. The EMDS-5 dataset has 21 types of EMs, each of which contains 20 original EM images, 20 single-object GT images and 20 multi-object GT images. EMDS-5 can realize to evaluate image preprocessing, image segmentation, feature extraction, image classification and image retrieval functions. In order to prove the effectiveness of EMDS-5, for each function, we select the most representative algorithms and price indicators for testing and evaluation. The image preprocessing functions contain two parts: image denoising and image edge detection. image denoising uses nine kinds of filters to denoise 13 kinds of noises, respectively. In the aspect of edge detection, six edge detection operators are used to detect the edges of the images, and two evaluation indicators, peak-signal to noise ratio and mean structural similarity, are used for evaluation. image segmentation includes single-object image segmentation and multi-object image segmentation. Six methods are used for single-object image segmentation, while k-means and U-net are used for multi-object *** extract nine features from the images in EMDS-5 and use the Support Vector Machine classifier for testing. In terms of image classification, we select the VGG16 feature to test different classifiers. We test two types of retrieval approaches: texture feature retrieval and deep learning feature retrieval. We select the last layer of features of these two deep learning networks as feature vectors. We use mean average precision as the evaluation index for retrieval. © 2021, CC BY.

关键词： image segmentation

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Complex-valued Iris Recognition Network

arXiv

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arXiv 2020年

作者： Nguyen, Kien Fookes, Clinton Sridharan, Sridha Ross, Arun Image and Video Research Laboratory SAIVT School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD4000 Australia The Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode;however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture. © 2020, CC BY-NC-SA.

关键词： Biometrics

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Completely self-supervised crowd counting via distribution matching

arXiv

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arXiv 2020年

作者： Sam, Deepak Babu Agarwalla, Abhinav Joseph, Jimmy Sindagi, Vishwanath A. Babu, R. Venkatesh Patel, Vishal M. Video Analytics Lab Department of Computational and Data Sciences Indian Institute of Science Bangalore India Vision & Image Understanding Lab Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States

Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to map these features to the end task of density estimation. We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a large set of unlabeled crowd images, is the approximate upper limit of the crowd count for the given dataset. Our method dwells on the idea that natural crowds follow a power law distribution, which could be leveraged to yield error signals for backpropagation. A density regressor is first pretrained with self-supervision and then the distribution of predictions is matched to the prior by optimizing Sinkhorn distance between the two. Experiments show that this results in effective learning of crowd features and delivers significant counting performance. Furthermore, we establish the superiority of our method in less data setting as well. The code and models for our approach is available at https://***/val-iisc/css-ccnn. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Gashissdb: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer

arXiv

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arXiv 2021年

作者： Hu, Weiming Li, Chen Li, Xiaoyan Rahaman, Md Mamunur Ma, Jiquan Zhang, Yong Chen, Haoyuan Liu, Wanli Sun, Changhao Yao, Yudong Sun, Hongzan Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group MBIE College Northeastern University Shenyang110169 China Department of Pathology Cancer Hospital China Medical University Liaoning Cancer Hospital and Institute Shenyang110042 China Department of Computer Science and Technology Heilongjiang University Heilongjiang Harbin150080 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Department of Radiology Shengjing Hospital China Medical University Shenyang110122 China Institute of Medical Informatics University of Luebeck Luebeck Germany

Background and Objective: Gastric cancer has turned out to be the fifth most common cancer globally, and early detection of gastric cancer is essential to save lives. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, computer-aided diagnostic techniques are challenging to evaluate due to the scarcity of publicly available gastric histopathology image datasets. Methods: In this paper, a noble publicly available Gastric Histopathology Sub-size image Database (GasHisSDB) is published to identify classifiers’ performance. Specifically, two types of data are included: normal and abnormal, with a total of 245,196 tissue case images. In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation. Seven classical machine learning classifiers, three Convolutional Neural Network classifiers, and a novel transformer-based classifier are selected for testing on image classification tasks. Results: This study performed extensive experiments using traditional machine learning and deep learning methods to prove that the methods of different periods have discrepancies on GasHisSDB. Traditional machine learning achieved the best accuracy rate of 86.08% and a minimum of just 41.12%. The best accuracy of deep learning reached 96.47% and the lowest was 86.21%. Accuracy rates vary significantly across classifiers. Conclusions: To the best of our knowledge, it is the first publicly available gastric cancer histopathology dataset containing a large number of images for weakly supervised learning. We believe that GasHisSDB can attract researchers to explore new algorithms for the automated diagnosis of gastric cancer, which can help physicians and patients in the clinical setting. Copyright © 2021, The Authors. All rights reserved.

关键词： image classification

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QUBIQ: Uncertainty Quantification for Biomedical image Segmentation Challenge

arXiv

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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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Reduced myelin contributes to cognitive impairment in patients with monogenic small vessel disease

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Alzheimer's and Dementia 2025年第5期21卷 e70127页

作者： Denecke, Jannis Dewenter, Anna Lee, Jongho Franzmeier, Nicolai Valentim, Carolina Kopczak, Anna Dichgans, Martin Pirpamer, Lukas Gesierich, Benno Duering, Marco Ewers, Michael Institute for Stroke and Dementia Research (ISD) LMU University Hospital Munich Germany Laboratory for Imaging Science and Technology Department of Electrical and Computer Engineering Seoul South Korea Department of Psychiatry and Neurochemistry Institute of Neuroscience and Physiology The Sahlgrenska Academy University of Gothenburg Gothenburg Sweden Munich Cluster for Systems Neurology (SyNergy) Munich Germany German Center for Neurodegenerative Disease (DZNE) Munich Germany Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering University of Basel Basel Switzerland

INTRODUCTION: Myelin is pivotal for signal transfer and thus cognition. Cerebral small vessel disease (cSVD) is primarily associated with white matter (WM) lesions and diffusion changes;however, myelin alterations and related cognitive impairments in cSVD remain unclear. METHODS: We included 64 patients with familial cSVD (i.e., cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) and 20 cognitively unimpaired individuals. χ separation applied to susceptibility weighted imaging was used to assess myelin and iron within WM hyperintensities, normal appearing WM, and two strategic fiber tracts. Diffusion-based mean diffusivity and free water were analyzed for comparisons. Cognitive impairment was assessed by the Trail Making Test. RESULTS: CADASIL patients showed reduced myelin within WM hyperintensities and its penumbra in the normal appearing WM. Myelin was moderately correlated with diffusion and iron changes and associated with slower processing speed controlled for diffusion and iron alterations. DISCUSSION: Myelin constitutes WM alterations distinct from diffusion changes and substantially contributes to explaining cognitive impairment in cSVD. Highlights: χ-negative magnetic resonance signal was reduced within white matter hyperintensities and normal appearing white matter in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, suggesting widespread myelin decreases due to cerebral small vessel disease (cSVD). χ-negative values were only moderately associated with diffusion tensor imaging derived indices including free water and mean diffusivity, suggesting that χ separation depicts distinct microstructural changes in cSVD. Alterations in χ-negative values made a unique contribution to explain processing speed impairment, even when controlled for diffusion and iron changes. © 2025 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf

关键词： cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy chi separation diffusion magnetic resonance imaging susceptibility mapping white matter hyperintensities

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Cover image: Volume 55 Issue 10

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Lasers in Surgery and Medicine 2023年第10期55卷

作者： Arsham Hamidi PhD Yakub A. Bayhaqi PhD Sandra Drusová PhD Alexander A. Navarini MD, PhD Philippe C. Cattin PhD Ferda Canbaz PhD Azhar Zam PhD Biomedical Laser and Optics Group (BLOG) Department of Biomedical Engineering University of Basel Allschwil Switzerland Digital Dermatology Department of Biomedical Engineering University of Basel Allschwil Switzerland Department of Biomedical Engineering Center for medical Image Analysis and Navigation (CIAN) University of Basel Allschwil Switzerland Division of Engineering New York University Abu Dhabi Abu Dhabi UAE Department of Biomedical Engineering Department of Electrical and Computer Engineering Tandon School of Engineering New York University Brooklyn New York USA

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A comprehensive review for breast histopathology image analysis using classical and deep neural networks

arXiv

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arXiv 2020年

作者： Zhou, Xiaomin Li, Chen Rahaman, Md Mamunur Yao, Yudong Ai, Shiliang Sun, Changhao Li, Xiaoyan Wang, Qian Jiang, Tao Microscopic Image and Medical Image Analysis Group Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Pathology Department Liaoning Hospital & Institute Shenyang110042 China Control Engineering College Chengdu University of Information Technology Chengdu610103 China

Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological image analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

arXiv

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arXiv 2021年

作者： Liu, Wanli Li, Chen Rahaman, Md Mamunur Jiang, Tao Sun, Hongzan Wu, Xiangchen Hu, Weiming Chen, Haoyuan Sun, Changhao Yao, Yudong Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Shengjing Hospital China Medical University Shenyang110001 China Suzhou Ruiguan Technology Company Ltd. Suzhou215000 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Institute of Medical Informatics University of Luebeck Luebeck Germany

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Aspect ratio

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