Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9980221
Chenglin Wu, Xuran Zhou, Guannan Chen
The most important subtypes of joint abnormalities in patients with temporomandibular disorders are different forms of disc displacement and deformation. An effective segmentation model for jaw joint detection to support the diagnosis of TMJ disease on magnetic resonance imaging is very crucial. Data for this study were obtained from 204 MRI images of patients with articular discs and the corresponding MRI segmentation labels of the temporomandibular joints. These images were used to evaluate four deep learning-based semantic segmentation methods. Using a multi-scale structured C2Ftrans segmentation model transformed from coarse to fine, it describes medical image segmentation as a coarse to fine process. It is able to perform accurate target boundary segmentation with lower computational complexity. Tested on this dataset, comparing U-Net, Unet ++ and Attention-U net models for data segmentation results show the C2Ftrans model performs best with the highest dice of 73.5% and the lowest computational complexity.
{"title":"Coarse-to-Fine Tranformer for articular disc of the temporomandibular joint Segmentation","authors":"Chenglin Wu, Xuran Zhou, Guannan Chen","doi":"10.1109/CISP-BMEI56279.2022.9980221","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980221","url":null,"abstract":"The most important subtypes of joint abnormalities in patients with temporomandibular disorders are different forms of disc displacement and deformation. An effective segmentation model for jaw joint detection to support the diagnosis of TMJ disease on magnetic resonance imaging is very crucial. Data for this study were obtained from 204 MRI images of patients with articular discs and the corresponding MRI segmentation labels of the temporomandibular joints. These images were used to evaluate four deep learning-based semantic segmentation methods. Using a multi-scale structured C2Ftrans segmentation model transformed from coarse to fine, it describes medical image segmentation as a coarse to fine process. It is able to perform accurate target boundary segmentation with lower computational complexity. Tested on this dataset, comparing U-Net, Unet ++ and Attention-U net models for data segmentation results show the C2Ftrans model performs best with the highest dice of 73.5% and the lowest computational complexity.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123101254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9980307
Hongyu Zhao, Zhiping Yin
Aiming at the problem of low efficiency of remote sensing imagery for PV (Photovoltaic) panel extraction in desert areas, this paper proposes a remote sensing identification method for PV panels based on the optimization of multi-feature combinations, taking Qinghai province as an example. The research uses the GEE cloud platform to construct a feature set containing topographic features, spectral features and index features, filters the feature set according to the feature importance and recursive elimination idea, and introduces feature correlation analysis to filter the feature set to get the optimal feature combination, and uses random forest RF to achieve PV panel extraction, and designs four experiments to verify the effectiveness of the preferred features. The results show that: the best effect of PV panel extraction is achieved by the random forest algorithm with feature selection, the overall accuracy of classification reaches 95.86%, and the Kappa coefficient reaches 0.9197; and the accuracy of PV panel area extraction for Qinghai province can reach 95.68%; the feature optimization method proposed in this paper can effectively improve the extraction accuracy of PV panels in desert areas.
{"title":"Remote Sensing Extraction of Photovoltaic Panels in Desert Areas Based on Feature Optimization","authors":"Hongyu Zhao, Zhiping Yin","doi":"10.1109/CISP-BMEI56279.2022.9980307","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980307","url":null,"abstract":"Aiming at the problem of low efficiency of remote sensing imagery for PV (Photovoltaic) panel extraction in desert areas, this paper proposes a remote sensing identification method for PV panels based on the optimization of multi-feature combinations, taking Qinghai province as an example. The research uses the GEE cloud platform to construct a feature set containing topographic features, spectral features and index features, filters the feature set according to the feature importance and recursive elimination idea, and introduces feature correlation analysis to filter the feature set to get the optimal feature combination, and uses random forest RF to achieve PV panel extraction, and designs four experiments to verify the effectiveness of the preferred features. The results show that: the best effect of PV panel extraction is achieved by the random forest algorithm with feature selection, the overall accuracy of classification reaches 95.86%, and the Kappa coefficient reaches 0.9197; and the accuracy of PV panel area extraction for Qinghai province can reach 95.68%; the feature optimization method proposed in this paper can effectively improve the extraction accuracy of PV panels in desert areas.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125048858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9980349
Yingjie Li, Feng Li, Shuang Zhou, P. Miao, G. Jin
This paper presents a data acquisition and processing system for the STAR sTGC detector. The system will be used in the STAR detector forward upgrade program. In the STAR detector forward upgrade program, this data acquisition system is required to be able to read out the electric charge signal of 20,000 channels of the sTGC detector. And it is required to have the functions of configuring various parameters of each channel, filtering valid event data, storing data, real-time monitoring, amplitude distribution statistics and recovering the tracks of charged particles. According to these requirements, we designed a data acquisition system composed of 96 FEBs, 16 RODs and the acquisition software. This data acquisition system uses the VMM3a chip to realize the readout of the signal of the sTGC detector. The event data is transmitted to the ROD via the mini-SAS cable at a rate of 3.2Gbps. In ROD, a trigger window is generated to filter valid event data, and then time stamps are added. Finally, valid event data are transmitted to the acquisition software at a rate of 10Gbps via optical fiber. In functional test and cosmic ray test, it is proved that the data acquisition system meets the requirements of the STAR sTGC detector.
{"title":"Design of Data Acquisition and Signal Processing System for STAR sTGC Detector","authors":"Yingjie Li, Feng Li, Shuang Zhou, P. Miao, G. Jin","doi":"10.1109/CISP-BMEI56279.2022.9980349","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980349","url":null,"abstract":"This paper presents a data acquisition and processing system for the STAR sTGC detector. The system will be used in the STAR detector forward upgrade program. In the STAR detector forward upgrade program, this data acquisition system is required to be able to read out the electric charge signal of 20,000 channels of the sTGC detector. And it is required to have the functions of configuring various parameters of each channel, filtering valid event data, storing data, real-time monitoring, amplitude distribution statistics and recovering the tracks of charged particles. According to these requirements, we designed a data acquisition system composed of 96 FEBs, 16 RODs and the acquisition software. This data acquisition system uses the VMM3a chip to realize the readout of the signal of the sTGC detector. The event data is transmitted to the ROD via the mini-SAS cable at a rate of 3.2Gbps. In ROD, a trigger window is generated to filter valid event data, and then time stamps are added. Finally, valid event data are transmitted to the acquisition software at a rate of 10Gbps via optical fiber. In functional test and cosmic ray test, it is proved that the data acquisition system meets the requirements of the STAR sTGC detector.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129108048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9979921
Tian Gao, Xin Zhang, Cuiyun Du
We have numerically evaluated transmission property and bit error rate (BER) performance of 120Gbps Dual-Polarization Quadrature Amplitude Modulation (DP-16QAM) digital coherent signals, with and without nonlinear compensation using digital back propagation (DBP). If the maximum transmitter powers are defined as the powers at which BER floor levels are $1.0times 10^{-2}$ without error correction, the maximum transmitter power is +17.2 dBm for single-channel 120Gbps DP-16QAM formats in large-core and low-loss single-mode silica fibers nonrepeatered systems. There is 2 dB development compared without using DBP approach. However, the performance is affected by nonlinear interference in DWDM non-repeatered systems, the improvement has been reduced to 0.6dB due to disturbance from neighboring DWDM channels.
{"title":"On the Impact of Digital Back Propagation Nonlinearity Compensation in Non-Repeatered Transmission WDM Systems","authors":"Tian Gao, Xin Zhang, Cuiyun Du","doi":"10.1109/CISP-BMEI56279.2022.9979921","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9979921","url":null,"abstract":"We have numerically evaluated transmission property and bit error rate (BER) performance of 120Gbps Dual-Polarization Quadrature Amplitude Modulation (DP-16QAM) digital coherent signals, with and without nonlinear compensation using digital back propagation (DBP). If the maximum transmitter powers are defined as the powers at which BER floor levels are $1.0times 10^{-2}$ without error correction, the maximum transmitter power is +17.2 dBm for single-channel 120Gbps DP-16QAM formats in large-core and low-loss single-mode silica fibers nonrepeatered systems. There is 2 dB development compared without using DBP approach. However, the performance is affected by nonlinear interference in DWDM non-repeatered systems, the improvement has been reduced to 0.6dB due to disturbance from neighboring DWDM channels.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131600030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9979973
Yu Su
In allusion to the problems of the conventional electrocardiogram (ECG) acquisition system, such as large volume, high price and complexity, a portable ECG acquisition and display system based on ADS1292R chip and STM32F103 is designed in the paper. The system uses ASD1292R as analog front end, processes the signal through STM32 microcontroller, and displays heart rate and ECG on the TFT-LCD. Moving average filter and FIR band-pass filter algorithm are adopted in order to remove interference signal. At the same time, the ECG signal can be transmitted to the mobile APP by the serial port Bluetooth module, user can observe their own heart rate and ECG waveform in real time and understand their own physical condition clearly. Through the experiment test, the relative error of heart rate measurement is less than 3%. The research results show that the designed system can stably and effectively collect the ECG signal of human body, and realize the signal transmission and display. It has the characteristics of small size, low cost and low power consumption and is convenient for daily use.
{"title":"Design of ECG acquisition and display system based on ADS1292R and STM32 microcontroller","authors":"Yu Su","doi":"10.1109/CISP-BMEI56279.2022.9979973","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9979973","url":null,"abstract":"In allusion to the problems of the conventional electrocardiogram (ECG) acquisition system, such as large volume, high price and complexity, a portable ECG acquisition and display system based on ADS1292R chip and STM32F103 is designed in the paper. The system uses ASD1292R as analog front end, processes the signal through STM32 microcontroller, and displays heart rate and ECG on the TFT-LCD. Moving average filter and FIR band-pass filter algorithm are adopted in order to remove interference signal. At the same time, the ECG signal can be transmitted to the mobile APP by the serial port Bluetooth module, user can observe their own heart rate and ECG waveform in real time and understand their own physical condition clearly. Through the experiment test, the relative error of heart rate measurement is less than 3%. The research results show that the designed system can stably and effectively collect the ECG signal of human body, and realize the signal transmission and display. It has the characteristics of small size, low cost and low power consumption and is convenient for daily use.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"314 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122340393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9980052
Wang Lintao, Yu Yuanhui, Guo Qisong, Li Xinxin
“Rita” is short for “right teacher AI” which is a learning assistant app developed for college teachers and students, aiming to provide an intelligent platform to assist teachers and students in learning and teaching. The system includes subject content tag graphic database, intelligent article push module, intelligent Q&A module, user service module, etc. This paper studies the structure, classification and application of knowledge map in the field of intelligent education, points out the practical efficacy of knowledge map in mobile teaching assistant system, and establishes a subject tree relationship model, which provides a basis for intelligent recommendation and subject analysis.
{"title":"An Application Of Knowledge Map In Intelligent Education","authors":"Wang Lintao, Yu Yuanhui, Guo Qisong, Li Xinxin","doi":"10.1109/CISP-BMEI56279.2022.9980052","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980052","url":null,"abstract":"“Rita” is short for “right teacher AI” which is a learning assistant app developed for college teachers and students, aiming to provide an intelligent platform to assist teachers and students in learning and teaching. The system includes subject content tag graphic database, intelligent article push module, intelligent Q&A module, user service module, etc. This paper studies the structure, classification and application of knowledge map in the field of intelligent education, points out the practical efficacy of knowledge map in mobile teaching assistant system, and establishes a subject tree relationship model, which provides a basis for intelligent recommendation and subject analysis.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122458675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hypokalemia is one of the most common electrolyte disorders in clinic. The detection of hypokalemia mainly depends on the detection of serum potassium concentration. Previous studies have shown that with the decrease of serum potassium ion concentration, ECG will show corresponding characteristics. In this paper, 12-lead ECG is used for intelligent detection of hypokalemia. After six artificial features based on ECG are extracted, a two-stream deep learning model is trained by using these features and 12-lead ECG to detect hypokalemia. The AVC of the two-stream model on the verification set is 0.84, and the AVC on the test set is 0.82. After taking the best working point, on the verification set, the sensitivity is 81.45%, the specificity is 74.21 %, and the recognition accuracy is 77.82%, while on the test set, the sensitivity is 77.54%, the specificity is 74.28%, and the recognition accuracy is 75.91 %. The results show that these time-domain features can significantly improve the recognition accuracy of hypokalemia.
{"title":"Intelligent Detection of Hypokalemia Based on 12-Lead ECG Using Two-stream Deep Learning Model","authors":"Yueyi Wang, Gaoyan Zhong, Ya’nan Wang, Qiang Zhu, Jiayong Xie, Xintao Deng, Aiguo Wang, Cuiwei Yang","doi":"10.1109/CISP-BMEI56279.2022.9980196","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980196","url":null,"abstract":"Hypokalemia is one of the most common electrolyte disorders in clinic. The detection of hypokalemia mainly depends on the detection of serum potassium concentration. Previous studies have shown that with the decrease of serum potassium ion concentration, ECG will show corresponding characteristics. In this paper, 12-lead ECG is used for intelligent detection of hypokalemia. After six artificial features based on ECG are extracted, a two-stream deep learning model is trained by using these features and 12-lead ECG to detect hypokalemia. The AVC of the two-stream model on the verification set is 0.84, and the AVC on the test set is 0.82. After taking the best working point, on the verification set, the sensitivity is 81.45%, the specificity is 74.21 %, and the recognition accuracy is 77.82%, while on the test set, the sensitivity is 77.54%, the specificity is 74.28%, and the recognition accuracy is 75.91 %. The results show that these time-domain features can significantly improve the recognition accuracy of hypokalemia.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116926868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9980193
Xiangqian Li, Jinping Sun, Fuyuan Feng
In the low altitude marine multi-target tracking scenario, the multiple hypothesis tracking (MHT) algorithm generates a large number of track segments due to the influence of sea clutter. To address this problem, an amplitude information aided graph-based track stitching method is proposed. First, the method models the track stitching scenario as a graph; after that, the amplitude information of different targets are passed in the track graph to obtain the association likelihood of amplitude information between track segments. Under the assumption of Markov, the association likelihood of the amplitude information between the track segments is multiplied with the association likelihood of the target states to obtain the association likelihood between the track segments. Finally, the minimum-cost maximum-flow (MCMF) algorithm is used to solve the stitching results. The simulation results show that the proposed algorithm can effectively improve the track stitching performance in the low altitude marine multi-target tracking scenario, and has a certain degree of improvement compared with the Hungarian algorithm in terms of rates of false association and target fragmentation.
{"title":"Graph-based Track Stitching Method for Low Altitude Marine Multi-target Tracking","authors":"Xiangqian Li, Jinping Sun, Fuyuan Feng","doi":"10.1109/CISP-BMEI56279.2022.9980193","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980193","url":null,"abstract":"In the low altitude marine multi-target tracking scenario, the multiple hypothesis tracking (MHT) algorithm generates a large number of track segments due to the influence of sea clutter. To address this problem, an amplitude information aided graph-based track stitching method is proposed. First, the method models the track stitching scenario as a graph; after that, the amplitude information of different targets are passed in the track graph to obtain the association likelihood of amplitude information between track segments. Under the assumption of Markov, the association likelihood of the amplitude information between the track segments is multiplied with the association likelihood of the target states to obtain the association likelihood between the track segments. Finally, the minimum-cost maximum-flow (MCMF) algorithm is used to solve the stitching results. The simulation results show that the proposed algorithm can effectively improve the track stitching performance in the low altitude marine multi-target tracking scenario, and has a certain degree of improvement compared with the Hungarian algorithm in terms of rates of false association and target fragmentation.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116511001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-05DOI: 10.1109/CISP-BMEI56279.2022.9979917
Xin Tang, Juan Gao, Fan Yang, Chenxi Hu
Muti-b-value Diffusion Weighted Imaging (DWI) is commonly used in clinical and neuroscientific applications. The traditional single-shot Echo-Planer Imaging (EPI) sequence suffers from low image resolution. Although the multi-shot EPI sequence can increase spatial resolution, the multi-shot k-space sampling causes linearly increased scan time. An interleaved EPI acquisition can significantly reduce the scan time; however, the dynamic change of image phase and image contrast causes aliasing artifacts. To improve the scan efficiency and preserve the image quality, an interleaved keyhole-EPI multi-b-value multi-shot sequence is proposed, with the image reconstruction formulated as a Locally Low Rank (LLR) constrained problem. The resultant cost function is minimized by a computationally efficient ADMM algorithm. The proposed method was compared with interleaved EPI acquisition using the state-of-the-art SPatial-Angular Locally Low Rank (SPA-LLR) algorithm in two healthy subjects. The results showed that the proposed method achieved superior image quality and fewer aliasing artifacts compared with the state-of-the-art method in both the raw DWI images and Apparent Diffusion Coefficient (ADC) maps.
多b值弥散加权成像(DWI)广泛应用于临床和神经科学领域。传统的单镜头回波平面成像(EPI)序列存在图像分辨率低的问题。虽然多镜头EPI序列可以提高空间分辨率，但多镜头k空间采样导致扫描时间线性增加。交错的EPI采集可以显著缩短扫描时间;然而，图像相位和图像对比度的动态变化会引起混叠伪影。为了提高扫描效率和保持图像质量，提出了一种交错keyhole-EPI多b值多镜头序列，并将图像重建表述为局部低秩约束问题。所得到的代价函数通过计算效率高的ADMM算法最小化。将该方法与基于空间-角度局部低秩(spatial - angle local Low Rank, SPA-LLR)算法的交错EPI采集方法在两名健康受试者身上进行了比较。结果表明，该方法在原始DWI图像和表观扩散系数(ADC)图上均取得了较好的图像质量和较少的混叠伪影。
{"title":"Acceleration of Multi-b-value Multi-shot Diffusion-weighted Imaging using Interleaved Keyhole-EPI and Locally Low Rank Reconstruction","authors":"Xin Tang, Juan Gao, Fan Yang, Chenxi Hu","doi":"10.1109/CISP-BMEI56279.2022.9979917","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9979917","url":null,"abstract":"Muti-b-value Diffusion Weighted Imaging (DWI) is commonly used in clinical and neuroscientific applications. The traditional single-shot Echo-Planer Imaging (EPI) sequence suffers from low image resolution. Although the multi-shot EPI sequence can increase spatial resolution, the multi-shot k-space sampling causes linearly increased scan time. An interleaved EPI acquisition can significantly reduce the scan time; however, the dynamic change of image phase and image contrast causes aliasing artifacts. To improve the scan efficiency and preserve the image quality, an interleaved keyhole-EPI multi-b-value multi-shot sequence is proposed, with the image reconstruction formulated as a Locally Low Rank (LLR) constrained problem. The resultant cost function is minimized by a computationally efficient ADMM algorithm. The proposed method was compared with interleaved EPI acquisition using the state-of-the-art SPatial-Angular Locally Low Rank (SPA-LLR) algorithm in two healthy subjects. The results showed that the proposed method achieved superior image quality and fewer aliasing artifacts compared with the state-of-the-art method in both the raw DWI images and Apparent Diffusion Coefficient (ADC) maps.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of aerial photography technology, its imaging quality is higher and higher, and the post-processing technology requirements for aerial images are getting higher and higher. Aerial image target recognition technology has been a hot research content in recent years. This technology relies on computer vision and image processing algorithm. But aerial images have certain particularities, including long shooting distances, complex image backgrounds, and variable target angles. The above factors can easily lead to indistinguishability between the target boundary and the background information of the aerial images. In order to solve that problem, a smooth edge feature information recognition method for aerial images is proposed. The energy fitting term related to the gray value inside and outside the curve is introduced, with that the method can get rid of the dependence of the detection operator as the stopping function of the curve evolution. In order to prevent the algorithm from falling into a local optimal solution in the iterative process, the Dirac function with a non-zero value in the domain is adopted. With synthetic and natural images, the effectiveness and accuracy of the method is verified. The robustness of the algorithm will be verified in the future researches by the acquired aerial image data set.
{"title":"Research on Smooth Edge Feature Recognition Method for Aerial Image Segmentation","authors":"Heng Wang, Yanrong Yuan, Chuangang Zhuang, Rui Shi, Jiamei Zhao, Xinyi Guo, Jintian Tang","doi":"10.1109/CISP-BMEI56279.2022.9980141","DOIUrl":"https://doi.org/10.1109/CISP-BMEI56279.2022.9980141","url":null,"abstract":"With the continuous development of aerial photography technology, its imaging quality is higher and higher, and the post-processing technology requirements for aerial images are getting higher and higher. Aerial image target recognition technology has been a hot research content in recent years. This technology relies on computer vision and image processing algorithm. But aerial images have certain particularities, including long shooting distances, complex image backgrounds, and variable target angles. The above factors can easily lead to indistinguishability between the target boundary and the background information of the aerial images. In order to solve that problem, a smooth edge feature information recognition method for aerial images is proposed. The energy fitting term related to the gray value inside and outside the curve is introduced, with that the method can get rid of the dependence of the detection operator as the stopping function of the curve evolution. In order to prevent the algorithm from falling into a local optimal solution in the iterative process, the Dirac function with a non-zero value in the domain is adopted. With synthetic and natural images, the effectiveness and accuracy of the method is verified. The robustness of the algorithm will be verified in the future researches by the acquired aerial image data set.","PeriodicalId":198522,"journal":{"name":"2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117048174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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