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arXiv 2020年

作者： Qin, Yulei Zheng, Hao Gu, Yun Huang, Xiaolin Yang, Jie Wang, Lihui Yao, Feng Zhu, Yue-Min Yang, Guang-Zhong Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province School of Computer Science and Technology Guizhou University Guiyang China Department of Thoracic Surgery Shanghai Chest Hospital Shanghai Jiao Tong University Shanghai China Université de Lyon INSA Lyon CREATIS CNRS INSERM UMR 5220 VilleurbanneU1206 France Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://***/News/56. © 2020, CC BY.

关键词： Computerized tomography

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2151

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Ultrasound in Medicine & Biology 2006年第5期32卷 P117-P117页

作者： K. Sun X. Yang L.P. Yao Q. Guo Shanghai Children’s Medical Center affiliated to School of Medicine Shanghai JiaoTong University China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

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AIM 2020 Challenge on image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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

arXiv

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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

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基于字典学习的图像去模糊研究

基于字典学习的图像去模糊研究

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第九届中国通信学会学术年会

作者： ZOU Jian-cheng 邹建成 CHE Dong-juan 车冬娟 Institute of Image Processing and Pattern Recognition North China University of TechnologyBeijing 1 北方工业大学图像处理与模式识别研究所北京 100144

图像在获取、传输等过程中会受到干扰，造成图像模糊不清。传统去模糊方法需要根据模糊原因估计出相应的点扩散函数(PSF)，然后进行反卷积估计出原始图像。本文借鉴K-SVD算法，利用对模糊图像进行训练得到自适应字典;并利用计算量相对... 详细信息

图像在获取、传输等过程中会受到干扰，造成图像模糊不清。传统去模糊方法需要根据模糊原因估计出相应的点扩散函数(PSF)，然后进行反卷积估计出原始图像。本文借鉴K-SVD算法，利用对模糊图像进行训练得到自适应字典;并利用计算量相对较小且收缩速度较快的PCD算法来求解稀疏解。实验结果表明，相对传统图像去模糊算法和固定字典算法，自适应的字典学习算法能达到更好的去模糊效果。

关键词：图像处理去模糊方法字典学习算法研究

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The figure source equation and its physical consideration

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SYSTEMS AND COMPUTERS IN JAPAN 1996年第9期27卷 83-93页

作者： Aoki, Y Iijima, T Member Japan Advanced Institute of Science and Technology Hokuriku Ishikawa Japan 923 Yasubim Aoki:graduated from the University of Kanazawa Faculty of Engineering Department of Electrical Engineering and Information Science in 1992 and received his Master's degree in 1994 from the Japan Advanced Institute of Science and Technology Hokuriku Department of Information Science where he is currently in the doctoral program engaged in research on pattern recognition. Honorary Member Taizo lijima:graduatedwith a B.E. degree from the Tokyo Institute of Technology in 1948 and received his Ph.D. degree later. He joined the Electro-Technical Laboratories in 1948 and since then has been involved in research on electromagnetic theory and pattern recognition theory image processing voice recognition and research and development of OCR. After serving as the head of Iijima Special Research Laboratories he became Professor at the Tokyo Institute of Technology in 1986. He because Professor Emeritus of that institute in 1986. He became Professor at Tokyo Engineering University and Professor Emeritus there in 1991. In 1991 he became Professor at the Japan Advanced Institute of Science and Technology and was appointed Vice President of the institute in 1992. He served successively as editor examiner secretary of general affairs inspector counsel committee-head of pattern recognition and specialized studies head of Tokyo chapter and vice president of this institute. In 1991 he became Honorary Member. He received the Paper Award from the Institute four times and the Literary Works Award once. He received the Achievement Award and the Distinguished Service Award in 1976 and 1989 respectively. He received the Purple Ribbon Medal from the Emperor in 1989.

Theoretical studies on Iijima's ''Theory of pattern recognition,'' which is known as a method of multiple similarity, have shown clearly that there are situations when it is difficult to determine the viewpoint and visual field, important parameters to introduce topological aspects into the pattern space, observing a figure itself. To cope with this problem, this paper proposes transforming the observed image from a figure f(r) to a second-order differential image rho(r), called ''figure source.'' The aim is to reconstruct the theory in such a way as to enable the application of similarity evaluation methods as in the conventional case, without compromising the current theory, and all the necessary proofs are given. In this paper, the figure source equation as well as rules representing its properties are developed, and several related physical quantities are redefined.

关键词： figure source determination of viewpoint and visual field second-order differential image theory reconstruction figure source equation

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Scene matching based on non-linear pre-processing on reference image and sensed image

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Journal of Systems Engineering and Electronics 2005年第2期000卷

作者： ZHONG Sheng Zhang Tianxu Sang Nong State Key Lab for Image Processing & Intelligent Control， Institute for Pattern Recognition and Artificial Intelligence，Huazhong University of Science and Technology， Wuhan 430074， P. R. China

To solve the heterogeneous image scene matching problem, a non-linear pre-processing method for the original images before intensity-based correlation is proposed. The result shows that the proper matching probability... 详细信息

关键词： intensity-based correlation heterogeneous image scene matching

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MODEL-BASED OBJECT recognition USING BASIC PROBABILITY ASSIGNMENT

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SYSTEMS AND COMPUTERS IN JAPAN 1995年第12期26卷 49-57页

作者： YAMANE, S AOKI, K IZUMI, M FUKUNAGA, K Associate Member College of Engineering University of Osaka Prefecture Sakai Japan 593 Member Members Kunio Fukunafa:received his B.E. M.E. and Dr. of Eng. degrees in Electrical Engineering from University of Osaka Prefecture Osaka Japan 1967 1969 and 1973 respectively. He has been with the University of Osaka Prefecture since 1969 where he is now a Professor in the Department of Information and Systems Sciences. His current research interests include pattern recognition and image processing. He is a member of the Institute of Electronics Information and Communication Engineers of Japan the Information Processing Society of Japan the Japanese Society for Artificial Intelligence and the IEEE.

A method of object recognition is proposed based on Dempster-Shafer theory (DS theory), which can treat the ambiguity of image data. A model based object recognition technique based on an aspect imaging method will also be proposed. The aspect imaging method recognizes a similar aspect object by comparing an input image with an aspect image of the model database. Our proposed method calculates the degree of similarity based on the DS theory instead of Bayes theory, represents an uncertainty factor of similarity as a basic probability of DS theory, and assigns a basic probability of information to be decided. The experimental results show that the proposed method is both more robust and more reliable.

关键词： OBJECT recognition DEMPSTER-SHAFER THEORY image AMBIGUITY

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CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

arXiv

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

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Fundamental theory of natural observation method with complete reconstruction property by finite sum - Natural observation theory of normal type

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ELECTRONICS AND COMMUNICATIONS IN JAPAN PART III-FUNDAMENTAL ELECTRONIC SCIENCE 1996年第11期79卷 84-96页

作者： Iijima, T Iwaki, M Japan Advanced Institute of Science and Technology Ishikawa Japan 923-12 Taim Iijima received his B.E. degree in Electrical Engineering from Tokyo Institute of Technology in 1948 and joined the Electrotechnical Laboratory. He received his Ph.D. degree in 1957. He was involved in research on electromagnetic theory pattern recognition theory image processing speech recoption and OCR research development. He became Chief of Densoken Iijima Special Research Group and then Professor at Tokyo Institute of Technology in 1972. He was Professor Emeritus in 1986 Professor at Tokyo Engineering University in 1986 Professor Emeritus in 1993 Professor at the Japan Advanced Institute of Science and Technology in 1993 Vice President of the Institute in 1994 Editinghvestigation/General Affairs Secretary Inspector Councilor Chairman of Pattern Recognition Research Committee Head of Tokyo Region Vice President at I.E.I.C.E. Japan and became Honorary Member in 1993. He received the Paper Award four times Authorship Award once Achievement Award in 1976 Meritorious Award in 1989 and the Purple-Ribbon Prize in 1989.

A theory of natural observation methods was originally formulated for two complementary systems: neighboring type and equilibrium type. This paper introduces a new system, normal-type system, that is different from the forementioned two types. One characteristic feature of the normal-type natural observation method is its ability to reconstruct an original waveform from the sum of a finite number of observation values. The natural observation theory of a normal type evolves naturally under the condition that a waveform and its power component can be expressed in the sum form. These meanings are discussed by comparing a frequency response of the normal-type natural observation transform with that of a conventional filter bank. Furthermore, a criterion for determining observation parameters in order to realize the natural observation of normal type under a given tolerance of error is described. It is believed that the proposed theory offers new insights into systematization of the natural observation theory.

关键词： natural observation method neighboring type equilibrium type normal type

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