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automatic segmentation of gas plumes from multibeam water column images using a U-shape network

Journal of Oceanology and Limnology 2023年第5期41卷 1753-1764页

作者： Fanlin YANG Feng WANG Zhendong LUAN Xianhai BU Sai MEI Jianxing ZHANG Hongxia LIU College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590China Key Laboratory of Ocean Geomatics Ministry of Natural ResourcesQingdao 266590China Key Laboratory of Marine Geology and Environment Center of Deep Sea ResearchInstitute of OceanologyChinese Academy of SciencesQingdao 266071China Qingdao Institute of Marine Geology China Geological SurveyMinistry of Natural ResourcesQingdao 266237China College of Mathematics and Systems Science Shandong University of Science and TechnologyQingdao 266590China

Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich *** detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold ***,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective ***,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas *** experiments are conducted among three traditional segmentation methods and two deep learning *** results show that the AP-UNet segmentation model can effectively suppress complicated water column noise *** segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.

关键词： multibeam water column image(WCI) gas plumes UNet automatic segmentation

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The Research of ECG Signal automatic segmentation Algorithm Based on Fractal Dimension Trajectory

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Journal of Measurement Science and Instrumentation 2010年第S1期1卷 139-142页

作者： Yin-jing GUO~1,Qian Cao~1,Peng Gao~1,Zhi-xin Cheng~1,Wei Xia~2 (1.School of information and electrical engineeringShandong University of Science and Technology Qingdao,China 2.Shandong Fengyuan Coal Industry & Electric Power CO.,LTDZaozhuang,China) School of Information and Electrical Engineering Shandong University of Science and Technology

In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put ***,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain *** Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.

关键词： ECG fractal theory fractal dimension trajectory automatic segmentation

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automatic and Robust segmentation of Multiple Sclerosis Lesions with Convolutional Neural Networks

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Computers, Materials & Continua 2021年第1期66卷 977-991页

作者： H.M.Rehan Afzal Suhuai Luo Saadallah Ramadan Jeannette Lechner-Scott Mohammad Ruhul Amin Jiaming Li M.Kamran Afzal M.Kamran Afzal School of Electrical Engineering and Computing University of NewcastleCallaghanNSW 2308Australia Hunter Medical Research Institute New Lambton HeightsNSW 2305Australia Department of Neurology John Hunter HospitalNew Lambton HeightsNSW 2305Australia CSIRO Data61 MarsfieldNSW 2122Australia School of Computer Science and Technology Xiamen UniversityXiamen361005China

The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the *** detection of lesions is very time consuming and lacks *** of the lesions are difficult to detect manually,especially within the grey *** paper proposes a novel and fully automated convolution neural network(CNN)approach to segment *** proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and *** first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase *** system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with *** routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI *** evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and *** and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art *** proposed method shows consistent higher precision and sensitivity than other *** proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.

关键词： Multiple sclerosis lesion segmentation automatic segmentation CNN automated tool lesion detection

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automatic Target segmentation based on Texture for Microscopic Images of Chinese Herbal Powders

Automatic Target Segmentation based on Texture for Microscop...

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第25届中国控制与决策会议

作者： Jun Li Yixu Song Yaoli Li Shaoqin Cai Zehong Yang State Key Laboratory of Intelligent Technology and Systems Computer Science and Technology Department Tsinghua University State Key Lab. of Natural and Biological Drugs School of Pharmaceutical Sciences Peking University

ISBN: (纸本)9781467355339

The identification of Chinese herbal powders is usually based on physical or chemical detection, but that is far from enough to identity dozens of herbal species. Microscopic images of these powders contain variety of information, and important evidence for identification. These images usually contain variety of substance, and most of them are noises, which makes the target segmentation become a difficult job. An effective automatic target segmentation algorithm based on texture is proposed in this paper. Our method consists of two steps: "Preliminary segmentation" and "Further segmentation". Firstly, feature vector of texture is extracted and clustered into two groups: background and foreground;secondly, taking the continuity of edge and the locality of target into consideration, energy equations are established, and Maximum flow-Minimum cut Algorithm is applied to solve them. Three groups of images are used to test our method: microscopic images of Chinese herbal powders, Brodaze Images, and natural texture images. And the experimental results show that our method achieves a better segmentation compared with Grab-Cut, and additionally user inter-action is not required in our method.

关键词： Chinese herbal powder microscopic images automatic segmentation texture feature

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Automated retinal layer segmentation on optical coherence tomography image by combination of structure interpolation and lateral mean filtering

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Journal of Innovative Optical Health Sciences 2021年第1期4卷 112-122页

作者： Yushu Ma Yingzhe Gao Zhaolin Li Ang Li Yi Wang Jian Liu Yao Yu Wenbo Shi Zhenhe Ma School of Computer Science and Engineering Northeastern University Shenyang 110169P.R.China Shenzhen Academy of Metrology&Quality Inspection Shenzhen 518055P.R.China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao 066004P.R.China

segmentation of layers in retinal images obtained by optical coherence tomography(OCT)has become an important clinical tool to diagnose ophthalmic ***,due to the sus-ceptibility to speckle noise and shadow of blood vessels etc.,the layer segmentation technology based on a single image still fail to reach a satisfactory *** propose a combination method of structure interpolation and lateral mean filtering(SI-LMF)to improve the signal-to-noise ratio based on one retinal *** performing one-dimensional lateral mean filtering to remove noise,structure interpolation was operated to eliminate thickness ***,we used boundary growth method to identify *** with existing segmentations,the method proposed in this paper requires less data and avoids the influence of *** automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects,which successfully identified the boundaries of 10 physio-logical layers,consistent with the results based on the manual determination.

关键词： Optical coherence tomography retinal layers automatic segmentation mean filtering

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Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

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Cancer Communications 2018年第1期38卷 632-642页

作者： Chaofeng Li Bingzhong Jing Liangru Ke Bin Li Weixiong Xia Caisheng He Chaonan Qian Chong Zhao Haiqiang Mai Mingyuan Chen Kajia Cao Haoyuan Mo Ling Guo Qiuyan Chen Linquan Tang Wenze Qiu Yahui Yu Hu Liang Xinjun Huang Guoying Liu Wangzhong Li Lin Wang Rui Sun Xiong Zou Shanshan Guo Peiyu Huang Donghua Luo Fang Qiu Yishan Wu Yijun Hua Kuiyuan Liu Shuhui Lv Jingjing Miao Yanqun Xiang Ying Sun Xiang Guo Xing Lv State Key Laboratory of Oncology in South China Collaborative Innovation Center of Cancer MedicineGuangzhou 510060P.R.China Department of Information Sun Yat-Sen University Cancer CenterGuangzhou 510060P.R.China Department of Radiology Sun Yat-Sen University Cancer CenterGuangzhou 510060P.R.China Department of Nasopharyngeal Carcinoma Sun Yat-Sen University Cancer CenterGuangzhou 510060P.R.China Precision Medicine Center Sun Yat-Sen University Cancer CenterGuangzhou 510060P.R.China Department of Radiotherapy Sun Yat-Sen University Cancer CenterGuangzhou 510060P.R.China Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy Guangzhou 510060P.R.China

Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,the positive rate for malignancy identification during biopsy is low,thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial ***,we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep ***:An endoscopic images-based nasopharyngeal malignancy detection model(eNPM-DM)consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and ***,a total of 28,966 qualified images were *** these images,27,536 biopsy-proven images from 7951 individuals obtained from January 1st,2008,to December 31st,2016,were split into the training,validation and test sets at a ratio of 7:1:2 using simple ***,1430 images obtained from January 1st,2017,to March 31st,2017,were used as a prospective test set to compare the performance of the established model against oncologist *** dice similarity coef-ficient(DSC)was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images,by comparing automatic segmentation with manual segmenta-tion performed by the ***:All images were histopathologically confirmed,and included 5713(19.7%)normal control,19,107(66.0%)nasopharyngeal carcinoma(NPC),335(1.2%)NPC and 3811(13.2%)benign *** eNPM-DM attained an overall accuracy of 88.7%(95%confidence interval(CI)87.8%-89.5%)in detecting malignancies in the test *** the prospective comparison phase,eNPM-DM outperformed the experts:the overall accuracy was 88.0%(95%CI 86.1%-89.6%)vs.80.5%(95%CI 77.0%-84.0%).The eNPM-DM required less time(40 s vs.110.0±5.8 min)and exhibited encouraging performance in

关键词： Nasopharyngeal malignancy Deep learning Differential diagnosis automatic segmentation

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A mathematical morphological approach for region of interest coding of microscopy image compression

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Journal of Harbin Institute of Technology(New Series) 2012年第3期19卷 115-121页

作者：夏伟强樊尚春邢维巍刘长庭李天志王俊峰 School of Instrumentation Science and Opto-electronics Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University General Hospital of the People's Liberation Army

A novel mathematical morphological approach for region of interest(ROI) automatic determination and JPEG2000-based coding of microscopy image compression is *** algorithm is very fast and requires lower computing power,which is particularly suitable for some irregular region-based cell microscopy images with poor ***,an active threshold-based method is discussed to create a rough mask of regions of interest(cells).And then some morphological operations are designed and applied to achieve the segmentation of *** addition,an extra morphological operation,dilation,is applied to create the final mask with some redundancies to avoid the"edge effect"after removing false ***,ROI and region of background(ROB) are obtained and encoded individually in different compression ratio flexibly based on the JPEG2000,which can adjust the quality between ROI and ROB without coding for ROI *** experimental results certify the effectiveness of the proposed algorithm,and compared with JPEG2000,the proposed algorithm has better performance in both subjective quality and objective quality at the same compression ratios.

关键词： mathematical morphology region of interest(ROI) automatic segmentation microscopy image compression JPEG2000

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automatic quantification of morphology on magnetic resonance images of the proximal tibia

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Medicine in Novel Technology and Devices 2023年第1期17卷 69-80页

作者： Dongdong He Yuan Guo Xushu Zhang Changjiang Wang Zihui Zhao Weiyi Chen Kai Zhang Binping Ji College of Biomedical Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Department of Engineering and Design University of SussexSussex HouseBrightonBN19RHUnited Kingdom Shanxi Hua Jin Orthopaedic Hospital Taiyuan030400ShanxiChina

The morphological quantification of the proximal tibia of the knee joint is important in knee *** knowledge of these parameters provides the basis for design of the tibial prosthesis and its ***,a prosthesis that is suitable for the morphological characteristics of Chinese knees is *** this paper,a deep learning automatic network framework is designed to achieve automatic segmentation and automatic quantitative analysis of magnetic resonance images of the *** enhanced feature fusion network structure is designed,including high and low-level feature fusion path modules to create accurate segmentation of the tibia.A new method of extracting feature points and lines from outline contours of the proximal tibia is designed to automatically calculate six clinical morphological linear parameters of the tibia in *** final result is an automatic visualisation of the tibial contour and automated extraction of tibial morphometric *** of the results from our system against a gold standard obtained by manual processing by expert clinicians showed the Dice coefficient to be 0.97,the accuracy to be 0.98,and the correlation coefficients for all six morphological parameters of the automatic quantification of the tibia are above *** gender-specific study found that the values of the proximal tibial linear parameters of internal and external tibial diameter,anterior and posterior diameter,lateral plateau length,lateral plateau width,medial plateau length,and medial plateau width in male patients are significantly greater than in female patients(all P values<0.01).The results enrich the use of deep learning in medicine,providing orthopaedic specialists with a valuable and intelligent quantitative tool that can assess the progression and changes in osteoarthritis of the knee joint.

关键词： Proximal tibia Feature fusion Quantitative analysis automatic segmentation Intelligent quantification

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An Improved Method for sEMG segmentation

An Improved Method for sEMG Segmentation

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第24届中国控制与决策会议

作者： Li Lin 1,2 , Wang Jianhui 1 , Dong Liang 1,2 1. College of Information Science and Engineering, Northeastern University, Shenyang 1100042. The Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University, Shenyang 110044

An improved method of continuous action’s sEMG automatic segmentation is presented in this paper. The maximum value and the integral value of local area are regarded as the distinguishing standard for start or stop action area, and search area adjusted dynamicly can speed up the sEMG segmentation. The experiment results show that, the methods which are proposed in the paper can achieve sEMG’s automatic segmentation, which consider less natural factors of action individual. The segmentation accuracy is higher, the effect is better, the time that is spent in the segmentation is shorter than the former method, and it provides more convenient for subsequent signal processing.

关键词： sEMG automatic segmentation Continuous Action

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一种无词典分词方法的分析与研究

一种无词典分词方法的分析与研究

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第二十二届中国数据库学术会议

作者：张玉连张敏张波燕山大学信息科学与工程学院

1引言自动分词一直是中文信息处理技术研究的一个热点和难点,从1980年以来,国内研究学者们就对文档的自动分词开展了大量研究,提出了许多自动分词方法。对语料库中相邻的字的组合频度进行统计,根据一定的频度计算公式来决定字符串成

关键词： automatic segmentation Statistic Dictionary

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