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arXiv 2021年

作者： Chen, Huai Wang, Renzhen Wang, Xiuying Li, Jieyu Fang, Qu Li, Hui Bai, Jianhao Peng, Qing Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science The University of Sydney SydneyNSW2006 Australia Department of Ophthalmology Shanghai Tenth People’s Hospital Tongji University Shanghai200240 China The Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China The Changchun GeneScience Pharmaceutical Co. LTD China

Contrastive learning, which aims to capture general representation from unlab.led images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified through optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performances. The source codes are publicly availab.e at https://***/HuaiChen-1994/LDLearning. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Feedback alfa-rooting algorithm for medical image enhancement 17

Feedback alfa-rooting algorithm for medical image enhancemen...

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17th image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology "STANKIN" Moscow Russia CUNY/ College of Staten Island Dept. of Computer Science New York United States

This paper presents a new combined local and global transform domain-based feedback image enhancement algorithm for medical diagnosis, treatment, and clinical research. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. The block size and alfa-rooting parameters driven through optimization using the Agaian's cost function (image enhancement non-reference quality measure). The presented new approach allows enhancing MRI and CT images with uneven lighting and brightness gradient by preserving the local image features/details. Extensive computer simulations (CS) on real medical images are offered to gage the presented method. CS shows that our method improves the contrast and enhances the details of the medical images effectively compared with the current state-of-art methods. © 2019, Society for Imaging Science and Technology.

关键词： Clinical research

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The quaternion-based anisotropic gradient for the color images 17

The quaternion-based anisotropic gradient for the color imag...

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17th image processing: Algorithms and Systems Conference, IPAS 2019

作者： Voronin, V. Zelensky, A. Agaian, S. Don State Technical University Lab. «Mathematical Methods of Image Processing and Computer Vision Intelligent Systems» Gagarina 1 Rostov on Don Russia Moscow State University of Technology"STANKIN" Moscow Russia CUNY College of Staten Island Dept. of Computer Science New York United States

image gradient, as a preprocessing step is an essential tool in image processing in many research areas such as edge detection, segmentation, smoothing, inpainting, etc. In the present paper, we develop a new gradient by integrated the quaternion framework with LPA-ICI (local polynomial approximation - the intersection of confidence intervals) based on anisotropic gradient concepts for the color image processing applications. Computer simulations on the Berkeley Segmentation Dataset show that the new quaternion anisotropic gradient exhibits fewer color artifacts compared to state-of-the-art techniques including well-knowledge gradient operators. © 2019, Society for Imaging Science and Technology.

关键词： Polynomial approximation

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllab.e trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalab.lity issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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DONet: Dual-octave network for fast MR image reconstruction

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yang, Zhanyuan Fu, Huazhu Xu, Yong Yang, Jian Shao, Ling Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China School of Automation Engineering University of Electronic Science and Technology of China 611731 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjiang University of Science and Technology Nanjiang210094 China Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Magnetic resonance (MR) image acquisition is an inherently prolonged process, whose acceleration has long been the subject of research. This is commonly achieved by obtaining multiple undersampled images, simultaneously, through parallel imaging. In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatialfrequency features from both the real and imaginary components of MR data, for parallel fast MR image reconstruction. More specifically, our DONet consists of a series of Dual-Octave convolutions (Dual-OctConv), which are connected in a dense manner for better reuse of features. In each Dual-OctConv, the input feature maps and convolutional kernels are first split into two components (i.e., real and imaginary), and then divided into four groups according to their spatial frequencies. Then, our Dual-OctConv conducts intra-group information updating and inter-group information exchange to aggregate the contextual information across different groups. Our framework provides three appealing benefits: (i) It encourages information interaction and fusion between the real and imaginary components at various spatial frequencies to achieve richer representational capacity. (ii) The dense connections between the real and imaginary groups in each Dual-OctConv make the propagation of features more efficient by feature reuse. (iii) DONet enlarges the receptive field by learning multiple spatial-frequency features of both the real and imaginary components. Extensive experiments on two popular datasets (i.e., clinical knee and fastMRI), under different undersampling patterns and acceleration factors, demonstrate the superiority of our model in accelerated parallel MR image reconstruction. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Improvement of the Human Action recognition Algorithm by the Pre-processing of Input Data

引用

IOP Conference Series: Materials Science and Engineering 2021年第1期1029卷

作者： M M Zhdanova O S Balab.eva V V Voronin E A Semenishchev A A Zelensky Center for Cognitive Technology and Machine Vision Moscow State University of Technology «STANKIN» Moscow Russia Lab. «Mathematical methods of image processing and intelligent computer vision systems» Don State Technical University Rostov-on-Don Russia

The paper presents an approach to recognizing human actions using an additional preprocessing stage of input data. The growing volumes of video information do not always allow support the quality of data at a high level; this can cause limitations in the further processing of digital data. In this regard, it becomes urgent to introduce an additional stage of image enhancement into the algorithm for recognizing actions in video. The proposed method includes three main steps: image enhancement, constructing a descriptor, and classification. The presented image enhancement stage is based on the combined local and global image processing in the frequency domain. The basic idea in using local alfa-rooting method is to apply it to different disjoint blocks with different sizes. To solve the problem of constructing a descriptor, a three-dimensional microblock dense difference (3D DMD) algorithm is used, which provides a highly oriented representation of image regions by tightly capturing microblocks within each region in several orientations and scales. 3D DMD has several advantages over other methods: higher efficiency compared to existing methods; minimal computational costs when using an integrated image; low dimension; ease of implementation; does not require settings. The presented modification allows to increase productivity by 2-4%.

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, lab.led datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have lab.ls of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, lab.ling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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PILAE: A non-gradient descent learning scheme for deep feedforward neural networks

arXiv

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arXiv 2018年

作者： Guo, Ping Wang, Ke Zhou, XiuLing The Image Processing & Pattern Recognition Lab. School of Systems Science Beijing Normal University Beijing100875 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Department of Technology and Industry Development Beijing City University Beijing100083 China

In this work, a non-gradient descent learning (NGDL) scheme was proposed for deep feedforward neural networks (DNN). It is known that an autoencoder can be used as the building blocks of the multi-layer perceptron (MLP) DNN, the MLP is taken as an example to illustrate the proposed scheme of pseudoinverse learning algorithm for autoencoder (PILAE) in this paper. The PILAE with low rank approximation is a NGDL algorithm, and the encoder weight matrix is set to be the low rank approximation of the pseudoinverse of the input matrix, while the decoder weight matrix is calculated by the pseudoinverse learning algorithm. It is worth to note that only very few network structure hyper-parameters need to be tuned compared with classical gradient descent learning algorithm. Hence, the proposed algorithm could be regarded as a quasi-automated training algorithm which could be utilized in automated machine learning field. The experimental results show that the proposed learning scheme for DNN could achieve better performance on considering the tradeoff between training efficiency and classification accuracy. Copyright © 2018, The Authors. All rights reserved.

关键词： Feedforward neural networks

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Thermal image Enhancement Algorithm Using Local And Global Logarithmic Transform Histogram Matching With Spatial Equalization

Thermal Image Enhancement Algorithm Using Local And Global L...

引用

IEEE Southwest Symposium on image Analysis and Interpretation

作者： Viacheslav Voronin Svetlana Tokareva Evgenii Semenishchev Sos Agaian Lab. "Mathematical methods of image processing and intelligent computer vision systems" Don State Technical University Rostov-on-Don Russian Federation Dept. of Computer Science CUNY/The College of Staten Island Staten Island New York United States

This paper presents a new thermal image enhancement algorithm based on combined local and global image processing in the frequency domain. The presented approach uses the fact that the relationship between stimulus and perception is logarithmic. The basic idea is to apply logarithmic transform histogram matching with spatial equalization approach on different image blocks. The resulting image is a weighted mean of all processing blocks. The weights for every local and global enhanced image driven through optimization of measure of enhancement (EME). Some presented experimental results illustrate the performance of the proposed algorithm on real thermal images in comparison with the traditional methods.

关键词： Histograms Transforms image enhancement Adaptive equalizers Frequency-domain analysis Computer vision

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A novel panoramic image stitching algorithm based on ORB

A novel panoramic image stitching algorithm based on ORB

引用

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Wang, Maosen Niu, Shaozhang Yang, Xuan Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

image stitching technique is to integrate multiple images with overlapping regions into a complete image with a wide viewing angle, less distortion, and no obvious suture. image stitching could be used for global positioning and robot autonomous navigation without changing the hardware. SIFT feature and SURF feature are the classical algorithm in the image stitching. But they have the long time-consuming problem. Facing this problem, a novel fast image stitching algorithm based on ORB (Oriented FAST and Rotated BRIEF) features is proposed in this paper. The algorithm firstly selects ORB algorithm which adds the direction information to the FAST detector for image feature extracting and matching. Secondly, RASANC (Random Sample Consensus) algorithm is used to eliminate false matching points. Finally, weighted average method is used to speed up the image fusion. Experimental results show that the stitching effect of the algorithm in this paper is the same as that of SIFT algorithm and SURF algorithm. The two obvious advantages of our algorithm include: (1) stitching speed is much faster than that of Shift and Surf;(2)The algorithm is robust to external parameters of camera. So the proposed algorithm is hopefully applied to some handed electrical devices. © 2017 IEEE.

关键词： image fusion

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