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A Target Detection Method in SAR images Based on Superpixel Segmentation

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A Target Detection Method in SAR Images Based on Superpixel ...

IEEE International Conference on Electronic Information and Communication technology (ICEICT)

作者： Ming Liu Shichao Chen Fugang Lu Mengdao Xing Jingbiao Wei School of Computer Science Shaanxi Normal University Xi'an China Xi'an Modern Control Technology Research Institute Xi'an China National Laboratory of Radar Signal Processing Xidian University Xi'an China Army Aviation Research Institute Beijing China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

A synthetic aperture radar (SAR) target detection method based on the fusion of multiscale superpixel segmentations is proposed in this paper. SAR images are segmented between land and sea firstly by using superpixel technology in different scales. Secondly, image segmentation results together with the constant false alarm rate (CFAR) detection result are coalesced. Finally, target detection is realized by fusing different scale results. The effectiveness of the proposed algorithm is tested on Sentinel-1A data.

关键词： image segmentation Conferences Object detection Detectors Radar polarimetry Information and communication technology Synthetic aperture radar

Infrared small-dim target detection with transformer under complex backgrounds

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arXiv 2021年

作者： Liu, Fangcen Gao, Chenqiang Chen, Fang Meng, Deyu Zuo, Wangmeng Gao, Xinbo The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China The Viterbi School of Engineering University of Southern California California90089 United States School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi’an710049 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China School of Computer Science and Technology Harbin Institute of Techonlogy Heilongjiang Harbin47822 China

The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets spread over the whole background, exploring the interaction information amongst image features in large-range dependencies to mine the difference between the target and background is crucial for robust detection. However, existing deep learning-based methods are limited by the locality of convolutional neural networks, which impairs the ability to capture large-range dependencies. Additionally, the small-dim appearance of the infrared target makes the detection model highly possible to miss detection. To this end, we propose a robust and general infrared small-dim target detection method with the transformer. We adopt the self-attention mechanism of the transformer to learn the interaction information of image features in a larger range. Moreover, we design a feature enhancement module to learn discriminative features of small-dim targets to avoid miss detection. After that, to avoid the loss of the target information, we adopt a decoder with the U-Net-like skip connection operation to contain more information of small-dim targets. Finally, we get the detection result by a segmentation head. Extensive experiments on two public datasets show the obvious superiority of the proposed method over state-of-the-art methods and the proposed method has stronger cross-scene generalization and anti-noise performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

Twisted-Planar Molecular Engineering with Sonication-Induced J-Aggregation To Design Near-Infrared J-Aggregates for Enhanced Phototherapy

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Angewandte Chemie 2024年第7期137卷

作者： Yubo Liu Yuchen Song Dr. Zhong-Hong Zhu Chao Ji Jianqing Li Hanyu Jia Prof. Yang Shi Prof. Fang Hu Prof. Zujin Zhao Prof. Dan Ding Prof. Ben Zhong Tang Prof. Guangxue Feng State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Luminescence from Molecular Aggregates School of Materials Science and Engineering South China University of Technology Guangzhou 510640 China These authors contributed equally to this work Biomaterials Research Center School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou 510515 China State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Bioactive Materials Ministry of Education and College of Life Sciences Nankai University Tianjin 300071 China School of Science and Engineering Shenzhen Institute of Aggregate Science and Technology The Chinese University of Hong Kong Shenzhen Shenzhen 518172 China

J-aggregates show great promise in phototherapy, but are limited to specific molecular skeletons and poor molecular self-assembly controllability. Herein, we report a twisted-planar molecular strategy with sonication-induced J-aggregation to develop donor–acceptor (D–A) type J-aggregates for phototherapy. With propeller aggregation-induced emission (AIE) moieties as the twisted subunits and thiophene as the planar π-bridge, the optimal twisted-planar π-interaction in MTSIC induces appropriate slip angle and J-aggregates formation, redshifting the absorption from 624 nm to 790 nm. In contrast, shorter π-planarity results in amorphous aggregates, and elongation promotes charge transfer (CT) coupled J-aggregates. Sonication was demonstrated to be effective in controlling self-assembly behaviors of MTSIC, which enables the transformation from amorphous aggregates to H-intermediates, and finally to stable J-aggregates. After encapsulation with lipid-PEG, the resultant J-dots show enhanced phototherapeutic effects over amorphous dots, including brightness, reactive oxygen species (ROS) generation, and photothermal conversion, delivering superior cancer phototherapy performance. This work not only advances D–A type J-aggregates design but also provides a promising strategy for supramolecular assembly development.

关键词： Self-assembly J-aggregation type I ROS donor-π-acceptor small molecules phototherapy

Noiseless linear amplification for the single-photon entanglement of arbitrary polarization–time-bin qudit

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Chinese Physics B 2019年第1期28卷 210-217页

作者： Ling-Quan Chen Yu-Bo Sheng Lan Zhou School of Science Nanjing University of Posts and TelecommunicationsNanjing 210003China College of Electronic and Optical Engineering&College of Microelectronics Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology(Ministry of Education) Nanjing University of Posts and TelecommunicationsNanjing 210003China Institute of Signal Processing Transmission Nanjing University of Posts and TelecommunicationsNanjing 210003China

Single-photon entanglement(SPE) is an important source in quantum communication. In this paper, we put forward a single-photon-assisted noiseless linear amplification protocol to protect the SPE of an arbitrary polarization–time-bin qudit from the photon transmission loss caused by the practical channel noise. After the amplification, the fidelity of the SPE can be effectively increased. Meanwhile, the encoded polarization–time-bin features of the qudit can be well preserved. The protocol can be realized under the current experimental conditions. Moreover, the amplification protocol can be extended to resist complete photon loss and partial photon loss during the photon transmission. After the amplification, we can not only increase the fidelity of the target state, but also solve the decoherence problem simultaneously. Based on the above features, our amplification protocol may be useful in future quantum communication.

关键词： single-photon entanglement noiseless linear amplification polarization–time-bin qudit complete and partial photon loss

dense feature aggregation and pruning for RGBT tracking

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arXiv 2019年

作者： Zhu, Yabin Li, Chenglong Luo, Bin Tang, Jin Wang, Xiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Institute of Physical Science and Information Technology Anhui University Hefei Anhui China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Key Laboratory of Industrial Image Processing and Analysis of Anhui Province Hefei Anhui China

How to perform effective information fusion of different modalities is a core factor in boosting the performance of RGBT tracking. This paper presents a novel deep fusion algorithm based on the representations from an end-to-end trained convolutional neural network. To deploy the complementarity of features of all layers, we propose a recursive strategy to densely aggregate these features that yield robust representations of target objects in each modality. In different modalities, we propose to prune the densely aggregated features of all modalities in a collaborative way. In a specific, we employ the operations of global average pooling and weighted random selection to perform channel scoring and selection, which could remove redundant and noisy features to achieve more robust feature representation. Experimental results on two RGBT tracking benchmark datasets suggest that our tracker achieves clear state-of-the-art against other RGB and RGBT tracking methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

Efficacy of a Novel Bioactive Glass-Polymer Composite for Enamel Remineralization following Erosive Challenge

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INTERNATIONAL JOURNAL OF DENTISTRY 2022年第0期2022卷 6539671页

作者： Fallahzadeh, Farnoosh Heidari, Soolmaz Najafi, Farhood Hajihasani, Maryam Noshiri, Nooshin Nazari, Neda F. Department of Operative Dentistry Faculty of Dentistry Qazvin University of Medical Sciences Qazvin Iran Department of Operative Dentistry Dental Caries Prevention Research Center Qazvin University of Medical Sciences Qazvin Iran Department of Resin and Additives Institute for Color Science and Technology P.O. Box: 16765-654 Tehran Iran Postgraduate Student of Operative Dentistry Student Research Committee Qazvin University of Medical Sciences Qazvin Iran Medical Image Processing Laboratory Alzahra University Tehran Iran Sharif University of Technology Central Lab Tehran Iran

Introduction. Dental caries is the most common cause of tooth loss. However, it can be stopped by enhancing remineralization. Fluoride and casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) are among the most important remineralizing agents. Recent studies have used bioactive glass as a remineralizing agent in different forms. This study aimed to assess the efficacy of a composite paste (prepared by mixing urethane polypropylene glycol oligomer with bioactive glass powder for easier application). Materials and Methods. Enamel disks were cut out of the buccal surface of extracted sound third molars. The samples were randomly divided into 3 groups of 15 and underwent Vickers microhardness test. X-ray diffraction (XRD) and field emission scanning electron microscopy/energy dispersive X-ray spectroscopy (FESEM/EDS) were performed. All samples were immersed in a demineralizing solution for 14 days. The tests were then repeated. Next, bioactive glass paste, fluoride, and CPP-ACP were applied on the surface of the samples and they were then stored in an artificial saliva for 14 days. The tests were repeated again. The microhardness values were analyzed using repeated measures ANOVA followed by one-way ANOVA and Tukey's post hoc test (P < 0.05). Results. The microhardness of the bioactive glass group was significantly higher than that of other groups (P < 0.05). XRD revealed an enamel structure more similar to sound enamel in the bioactive glass and CPP-ACP groups compared with the fluoride group. FESEM/EDS revealed higher hydroxyapatite deposition in the bioactive glass group than in the other two groups. Conclusions. All three remineralizing agents caused remineralization, but bioactive glass paste had a greater efficacy.

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Multi-Level Context Ultra-Aggregation for Stereo Matching

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Multi-Level Context Ultra-Aggregation for Stereo Matching

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Guang-Yu Nie Ming-Ming Cheng Yun Liu Zhengfa Liang Deng-Ping Fan Yue Liu Yongtian Wang Beijing Institute of Technology TKLNDST CS Nankai University National Key Laboratory of Science and Technology on Blind Signal Processing

ISBN: (纸本)9781728132945

Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, existing methods only use features from plain convolution layers or a simple aggregation of multi-level features to calculate cost volume, which is insufficient because stereo matching requires discriminative features to identify corresponding pixels in rectified stereo image pairs. In this paper, we propose a unary features descriptor using multi-level context ultra-aggregation (MCUA), which encapsulates all convolutional features into a more discriminative representation by intra-and inter-level features combination. Specifically, a child module that takes low-resolution images as input captures larger context information;the larger context information from each layer is densely connected to the main branch of the network. MCUA makes good usage of multi-level features with richer context and performs the image-to-image prediction holistically. We introduce our MCUA scheme for cost volume calculation and test it on PSM-Net. We also evaluate our method on Scene Flow and KITTI 2012/2015 stereo datasets. Experimental results show that our method outperforms state-of-the-art methods by a notable margin and effectively improves the accuracy of stereo matching.

关键词： branch main Volume Costs Unary multilayers stereo imaging Convolution

Efficient MedSAMs: Segment Anything in Medical images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

Relationship between pulmonary nodule malignancy and surrounding pleurae, airways and vessels: a quantitative study using the public LIDC-IDRI dataset

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arXiv 2021年

作者： Qin, Yulei Gu, Yun Zhang, Hanxiao Yang, Jie Wang, Lihui Wang, Zhexin Yao, Feng Zhu, Yue-Min Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China CREATIS INSA Lyon CNRS UMR 5220 INSERM U1206 Université de Lyon Villeurbanne69621 France Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang550025 China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai200025 China

Objectives: To investigate whether the pleurae, airways and vessels surrounding a nodule on non-contrast computed tomography (CT) can discriminate benign and malignant pulmonary nodules. Materials and Methods: The LIDC-IDRI dataset, one of the largest publicly available CT database, was exploited for study. A total of 1556 nodules from 694 patients were involved in statistical analysis, where nodules with average scorings 3 were respectively denoted as benign and malignant. Besides, 339 nodules from 113 patients with diagnosis ground-truth were independently evaluated. Computer algorithms were developed to segment pulmonary structures and quantify the distances to pleural surface, airways and vessels, as well as the counting number and normalized volume of airways and vessels near a nodule. Odds ratio (OR) and Chi-square (χ2) testing were performed to assess the correlation between features of surrounding structures and nodule malignancy. A non-parametric receiver operating characteristic (ROC) analysis was conducted in logistic regression to evaluate discrimination ability of each structure. Results: For the benign and malignant groups, the average distances from nodules to pleural surface, airways and vessels are respectively (6.56, 5.19), (37.08, 26.43) and (1.42, 1.07) mm. The correlation between nodules and the counting number of airways and vessels that contact or project towards nodules are respectively (OR=22.96, χ2=105.04) and (OR=7.06, χ2=290.11). The correlation between nodules and the volume of airways and vessels are (OR=9.19, χ2=159.02) and (OR=2.29, χ2=55.89). The areas-under-curves (AUCs) for pleurae, airways and vessels are respectively 0.5202, 0.6943 and 0.6529. Conclusion: Our results show that malignant nodules are often surrounded by more pulmonary structures compared with benign ones, suggesting that features of these structures could be viewed as lung cancer biomarkers. © 2021, CC BY.

关键词： Computerized tomography