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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： image segmentation

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Automatic diagnosis of disc herniation based on DenseNet fusion model 8

Automatic diagnosis of disc herniation based on DenseNet fus...

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8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： image segmentation

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Efficient MedSAMs: Segment Anything in Medical images on Laptop

arXiv

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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

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Using Laplacian spectrum to analyse the comorbidities network of hemorrhagic stroke

Using Laplacian spectrum to analyse the comorbidities networ...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Attila Bérczes Tamás Bérczes Imre Varga Attila Tiba Judit Zsuga Institute of Mathematics University of Debrecen Debrecen Hungary Department of Information Technology University of Debrecen Debrecen Hungary Department of Computer Graphics and Image Processing University of Debrecen Debrecen Hungary Department of Health Systems Management and Quality Management in Health Care University of Debrecen Debrecen Hungary

We analyze a database containing medical data of patients who suffered hemorrhagic stroke. From the original database we built the corresponding comorbidities network, and we analyzed this network using a graph theore... 详细信息

ISBN: (数字)9781728147932

ISBN: (纸本)9781728147949

关键词： Eigenvalues and eigenfunctions Laplace equations Hemorrhaging Symmetric matrices Databases Diseases Medical diagnostic imaging

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Rail surface defects detection based on gray scale gradient characteristics of image

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2018年第4期39卷 220-229页

作者： Min, Yongzhi Yue, Biao Ma, Hongfeng Xiao, Benyu School of Automation and Electrical Engineering Lanzhou Jiao tong University Lanzhou730070 China Rail Transit Electrical Automation Engineering Laboratory of Gansu Province Lanzhou Jiaotong University Lanzhou730070 China Gansu Provincial Engineering Research Center for Artifical Intelligence and Graphics & Image Processing Lanzhou730070 China School of Electronic Information Engineering Lanzhou Institute of Technology Lanzhou730050 China

When the machine vision technology is adopted to detect rail surface defects, the accuracy of results is easily influenced by the change of illumination, uneven reflection of rail surface, rust, etc. Therefore, this paper proposes a detection method for rail surface defection based on gray scale gradient characteristics of image. Firstly, the gray scale and gradient characteristics of different regions in the rail image are analyzed with the proposed defects detection device. Then, the background smoothing filter is designed based on the idea of bilateral filtering. By using local gray scale and gradient change information to adjust the smoothing degrees of different feature region adaptively, the original image is smoothed to generate a background image. Finally, image difference is made. The detection of rail surface defects is realized by setting threshold for the differential image and using connected-component labeling method. The experimental results show that the defects region in the image can be highlighted. Meanwhile, the proposed method can reduce the impact of illumination change, uneven reflection of rail surface, rust, etc. Both scar and crack defects in different track environments have been detected effectively. The undetected rate and false detection rate are 5.79% and 6.84%, respectively, which mean that the proposed method has certain practical value. © 2018, Science Press. All right reserved.

关键词： Surface defects

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Reeb graphs through local binary patterns 10

Reeb graphs through local binary patterns

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10th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2015

作者： Janusch, Ines Kropatsch, Walter G. Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

This paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. The nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such critical points using Local Binary Patterns. For one pixel the Local Binary Pattern feature vector is derived comparing this pixel to its neighbouring pixels in an environment of a certain radius. We start with an initial segmentation and a medial axis representation. Along this axis critical points are computed using Local Binary Patterns with the radius, defining the neighbouring pixels, set a bit larger than the radius according to the medial axis transformation. Critical points obtained in this way form the node set in a Reeb graph, edges are given through the connectivity of the skeleton. This approach aims at improving the representation of flawed segmented data. In the same way segmentation artefacts, as for example single pixels representing noise, may be corrected based on this analysis. © Springer International Publishing Switzerland 2015.

关键词： image segmentation

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Automated multi-contrast brain pathological area extraction from 2D MR images

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Journal of Applied Research and technology 2015年第1期13卷 58-69页

作者： Dvořák, P. Bartušek, K. Kropatsch, W.G. Smékal, Z. Institute of Scientific Instruments of the ASCR V.v.i Academy if Sciences of the Czech Republic Brno Czech Republic Dept. of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Pattern Recognition and Image Processing Group Institute of Computer Graphics and Algorithms Faculty of Informatics Vienna University of Technology Vienna Austria

The aim of this work is to propose the fully automated pathological area extraction from multi-parametric 2D MR images of brain. The proposed method is based on multi-resolution symmetry analysis and automatic thresholding. The proposed algorithm first detects the presence of pathology and then starts its extraction. T2 images are used for the presence detection and the multi-contrast MRI is used for the extraction, concretely T2 and FLAIR images. The extraction is based on thresholding, where Otsu's algorithm is used for the automatic determination of the threshold. Since the method is based on symmetry, it works for both axial and coronal planes. In both these planes of healthy brain, the approximate left-right symmetry exists and it is used as the prior knowledge for searching the approximate pathology location. It is assumed that this area is not located symmetrically in both hemispheres, which is met in most cases. The detection algorithm was tested on 203 T2-weighted images and reached the true positive rate of 87.52% and true negative rate of 93.14%. The extraction algorithm was tested on 357 axial and 443 coronal real images from publicly available BRATS databases containing 3D volumes brain tumor patients. The results were evaluated by Dice Coefficient (axial: 0.85 ± 0.11, coronal 0.82 ± 0.18) and by Accuracy (axial: 0.96 ± 0.05, coronal 0.94 ± 0.09). © 2015 Universidad Nacional Autónoma de México.

关键词： Brain Pathology Brain Tumor MRI Multi-contrast MRI Symmetry Analysis

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Fuzzy partition and correlation for image segmentation with differential evolution

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2013年第3期40卷 164-172页

作者： Nie, Fangyan Zhang, Pingfeng Institute of Graphics and Image Processing Technology Hunan University of Arts and Science Changde 415000 China College of Computer Science and Technology Hunan University of Arts and Science Changde 415000 China

Thresholding-based techniques have been widely used in image segmentation. The selection of appropriate threshold is a very significant issue for image thresholding. In this paper, a new image histogram thresholding method based on fuzzy partition and maximum correlation criterion is presented. In the proposed approach, the regions, i.e. object and background, are considered ambiguous in nature, and hence the regions are transformed into fuzzy domain with membership functions. Then, the fuzzy correlations about regions are constructed and the optimal threshold is determined by searching an optimal parameter combination of the membership functions such that the correlation of the fuzzy partitions is maximized. Since the exhaustive search for all fuzzy parameter combinations is too costly, the differential evolution algorithm is introduced into fuzzy correlation image segmentation to solve this optimal problem adaptively. Experimental results on general images and infrared images demonstrate the effectiveness of the proposed method.

关键词： image segmentation

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Human object extraction using nonextensive fuzzy entropy and chaos differential evolution

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, image processing and Pattern Recognition 2013年第2期6卷 43-54页

作者： Nie, Fangyan Li, Jianqi Rong, Qiusheng Pan, Meisen Zhang, Fen Institute of Graphics and Image Processing Technology College of Computer Science and Technology Hunan University of Arts and Science China

Human object extraction from infrared image has broad applications, and has become an active research area in image processing community. Combined with chaos differential evolution (CDE) algorithm and morphological operators, a novel infrared human target extraction method is proposed based on nonextensive fuzzy entropy. Firstly, the image was transformed into a fuzzy domain by fuzzy membership function, and the image nonextensive fuzzy entropy was constructed. Then, the image was segmented by thresholding based on the maximum entropy principle and the pseudoadditivity rule of nonextensive entropy. In order to reduce the search time of optimal threshold selection, the CDE algorithm was presented. Finally, the object was extracted using morphological operators to denoise, fill cavity on the threshold segmented image. Experimental results show that the proposed method is efficient and requires less computation time.

关键词： Evolutionary algorithms

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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2012 19th International Conference on Systems, Signals and image processing, IWSSIP 2012

作者： Shalunts, Gayane Haxhimusa, Yll Sablatnig, Robert Institute of Computer Aided Automation Computer Vision Lab. Vienna University of Technology Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab. Vienna University of Technology Austria

ISBN: (纸本)9783200023284

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate. © 2012 institute of Telecommunica.

关键词： Classification (of information)

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