ing is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through
{"title":"Copyright page","authors":"","doi":"10.1111/poms.12467","DOIUrl":"https://doi.org/10.1111/poms.12467","url":null,"abstract":"ing is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1963-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121973316","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6708041
Y. C. See, N. Noor, A. Lai
Face detection for low quality images and different face positions is a very challenging task. This paper presents a hybrid method for face detection to these problems. The algorithm starts with image resizing process followed by the Gaussian Mixture Model to calculate the skin likelihood value of pixel in an image. Then, the skin regions are extracted from the background with a proper threshold value obtained adaptively based on image information. This study developed an algorithm to performed face location and detection. This study used face database from University of Ljubljana (Slovenia) Computer Vision Laboratory (CVL), which contains seven 2D images corresponding to 114 different individuals, to evaluate the proposed system. The resolution of the images is 640*480 pixels. Another database, the Bao database which consists of 157 images with image resolutions within 57×85 pixels and 300 × 300 pixels is chosen. The detection accuracy for frontal face and side face images on CVL database is 94.4% and 84.7% respectively. The detection accuracy on Bao database is 93.6%.
{"title":"Hybrid face detection with skin segmentation and edge detection","authors":"Y. C. See, N. Noor, A. Lai","doi":"10.1109/ICSIPA.2013.6708041","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708041","url":null,"abstract":"Face detection for low quality images and different face positions is a very challenging task. This paper presents a hybrid method for face detection to these problems. The algorithm starts with image resizing process followed by the Gaussian Mixture Model to calculate the skin likelihood value of pixel in an image. Then, the skin regions are extracted from the background with a proper threshold value obtained adaptively based on image information. This study developed an algorithm to performed face location and detection. This study used face database from University of Ljubljana (Slovenia) Computer Vision Laboratory (CVL), which contains seven 2D images corresponding to 114 different individuals, to evaluate the proposed system. The resolution of the images is 640*480 pixels. Another database, the Bao database which consists of 157 images with image resolutions within 57×85 pixels and 300 × 300 pixels is chosen. The detection accuracy for frontal face and side face images on CVL database is 94.4% and 84.7% respectively. The detection accuracy on Bao database is 93.6%.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091416","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6707969
Xianling Dong, W. H. M. Saad, W. Adnan, S. Hashim, Nor Pai'za Mohd. Hassan, A. Nordin, M. I. Saripan
In a typical scintillation camera system, intrinsic resolution is dependent upon the accuracy of the identification of an interaction position. This paper intends to set up a evaluation tool based on Monte Carlo simulation for the purpose of estimating the intrinsic resolution of scintillation cameras. Monte Carlo N-Particles (MCNP) Code was applied to simulate the components of the model platform referred to Toshiba GCA-7100A with a monolithic Sodium Iodide (NaI) scintillator (40 × 40 × 0.9525 cm3). The simulation result was 3.7 mm full width at half maximum (FWHM) for intrinsic resolution which came out to be in a good agreement with the experimental result. This suggests that our proposed evaluation tool may help to optimize the parameters of the detector without physical experiments.
{"title":"Simulation of intrinsic resolution of scintillation camera in Monte Carlo environment","authors":"Xianling Dong, W. H. M. Saad, W. Adnan, S. Hashim, Nor Pai'za Mohd. Hassan, A. Nordin, M. I. Saripan","doi":"10.1109/ICSIPA.2013.6707969","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707969","url":null,"abstract":"In a typical scintillation camera system, intrinsic resolution is dependent upon the accuracy of the identification of an interaction position. This paper intends to set up a evaluation tool based on Monte Carlo simulation for the purpose of estimating the intrinsic resolution of scintillation cameras. Monte Carlo N-Particles (MCNP) Code was applied to simulate the components of the model platform referred to Toshiba GCA-7100A with a monolithic Sodium Iodide (NaI) scintillator (40 × 40 × 0.9525 cm3). The simulation result was 3.7 mm full width at half maximum (FWHM) for intrinsic resolution which came out to be in a good agreement with the experimental result. This suggests that our proposed evaluation tool may help to optimize the parameters of the detector without physical experiments.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121862966","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6708042
Chollette C. Chude-Olisah, G. Sulong, U. Chude-Okonkwo, S. Z. M. Hashim
In this paper, an illumination normalization technique for edge-based face recognition on face images with non-uniform illumination conditions, is proposed. The proposed illumination normalization technique fuses the merits of color (Red, Green and Blue) normalization (Nrgb) and gamma correction (GC) for color images. By the fusion of these methods the image becomes independent of the change in face images due to illumination direction. In that way, the presence of false edges in gradient faces is reduced. Experimental results on Georgia Tech Face database with illumination problem shows that the proposed technique improved significantly recognition accuracy in comparison to histogram equalization (HE), logarithm transform (LT) and gamma correction (GC).
{"title":"Illumination normalization for edge-based face recognition using the fusion of RGB normalization and gamma correction","authors":"Chollette C. Chude-Olisah, G. Sulong, U. Chude-Okonkwo, S. Z. M. Hashim","doi":"10.1109/ICSIPA.2013.6708042","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708042","url":null,"abstract":"In this paper, an illumination normalization technique for edge-based face recognition on face images with non-uniform illumination conditions, is proposed. The proposed illumination normalization technique fuses the merits of color (Red, Green and Blue) normalization (Nrgb) and gamma correction (GC) for color images. By the fusion of these methods the image becomes independent of the change in face images due to illumination direction. In that way, the presence of false edges in gradient faces is reduced. Experimental results on Georgia Tech Face database with illumination problem shows that the proposed technique improved significantly recognition accuracy in comparison to histogram equalization (HE), logarithm transform (LT) and gamma correction (GC).","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130214466","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6708009
Ashrani Aizzuddin Abd. Rahni, E. Lewis, K. Wells
Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. The preliminary results of the framework is presented and discussed with proposals for future work.
{"title":"Development of a semi-automated segmentation framework for thoracic-abdominal organs","authors":"Ashrani Aizzuddin Abd. Rahni, E. Lewis, K. Wells","doi":"10.1109/ICSIPA.2013.6708009","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708009","url":null,"abstract":"Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. The preliminary results of the framework is presented and discussed with proposals for future work.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128108670","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6707993
Z. Abidin, M. Manaf, A. S. Shibghatullah, S. Anawar, R. Ahmad
Iris recognition is the most accurate biometric identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detectors which commonly used. The objectives of this research are to a) study the edge detection criteria and b) measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with [320×280] dimension is obtained from the CASIA database which has been pre-processed through the segmentation and normalization in obtaining the rubber sheet model with [20×240] in dimension. Once it has been produced, the important information is extracted from the iris. Results show that, the PSNR values of iris feature before and after the process of extraction, was 24.93 and 9.12. For sobel and prewitt, both give 18.5 after the process. Based on our findings, the impact of edge detection techniques produces higher accuracy in iris recognition system.
{"title":"Feature extraction from epigenetic traits using edge detection in iris recognition system","authors":"Z. Abidin, M. Manaf, A. S. Shibghatullah, S. Anawar, R. Ahmad","doi":"10.1109/ICSIPA.2013.6707993","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707993","url":null,"abstract":"Iris recognition is the most accurate biometric identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detectors which commonly used. The objectives of this research are to a) study the edge detection criteria and b) measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with [320×280] dimension is obtained from the CASIA database which has been pre-processed through the segmentation and normalization in obtaining the rubber sheet model with [20×240] in dimension. Once it has been produced, the important information is extracted from the iris. Results show that, the PSNR values of iris feature before and after the process of extraction, was 24.93 and 9.12. For sobel and prewitt, both give 18.5 after the process. Based on our findings, the impact of edge detection techniques produces higher accuracy in iris recognition system.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128065762","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6707970
S. Ali, Xianling Dong, A. Noor, F. Rokhani, S. Hashim, M. I. Saripan
Silicone photomultiplier (SiPM) technology have been introduced recently for photons detection. This type of detector offer various advantages compare to the conventional photomultiplier tubes (PMTs). SiPM are smaller in size and thus consumes less space. Several researches have been conducted using SiPM for image acquisition in the field of medical imaging. The aim of this research is to model the intrinsic resolution of a scintillation camera using SiPM detector. Experiments are conducted to determine the optimum distance between the light source and the SiPM detector to obtain an intrinsic resolution of 3.7 mm. The resolution is base on previous research using Toshiba GCA 7100A platform with Sodium Iodide (NaI) scintillator (40 × 40 × 0.9525 cm3). Results revealed that the SiPM needs to be placed at a distance of 14.36 mm from the light source to represent the scintillation camera intrinsic resolution. It is concluded that the SiPM detector can be used to model the current scintillation camera intrinsic resolution and have a huge potential to replace the current photomultiplier tube detector.
{"title":"Modelling of light photons detection in scintillation camera","authors":"S. Ali, Xianling Dong, A. Noor, F. Rokhani, S. Hashim, M. I. Saripan","doi":"10.1109/ICSIPA.2013.6707970","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707970","url":null,"abstract":"Silicone photomultiplier (SiPM) technology have been introduced recently for photons detection. This type of detector offer various advantages compare to the conventional photomultiplier tubes (PMTs). SiPM are smaller in size and thus consumes less space. Several researches have been conducted using SiPM for image acquisition in the field of medical imaging. The aim of this research is to model the intrinsic resolution of a scintillation camera using SiPM detector. Experiments are conducted to determine the optimum distance between the light source and the SiPM detector to obtain an intrinsic resolution of 3.7 mm. The resolution is base on previous research using Toshiba GCA 7100A platform with Sodium Iodide (NaI) scintillator (40 × 40 × 0.9525 cm3). Results revealed that the SiPM needs to be placed at a distance of 14.36 mm from the light source to represent the scintillation camera intrinsic resolution. It is concluded that the SiPM detector can be used to model the current scintillation camera intrinsic resolution and have a huge potential to replace the current photomultiplier tube detector.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129981366","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6708023
Y. M. Chee, A. Sha'ameri
The quadratic time-frequency distribution (TFD) provides distribution of energy over the time-frequency plane for time-varying signals. Since phase information is not represented, the cross TFD (XTFD) is proposed to analyze phase shift keying (PSK) signals by providing localized phase information. However, the phase estimation does not yield desirable performances as the time-frequency representation is interfered by duplicated terms. The problem is solved by the proposed XTFD which uses an adaptive window to remove the duplicated term. The local and global adaptive algorithms are proposed to estimate the window width. It is shown that both algorithms meet the theoretical limit at a minimum signal-to-noise ratio (SNR) of 5dB. At lower SNR, the local adaptive method outperforms the global adaptive method at the expense of higher number of computation.
{"title":"Phase estimation of PSK signals using XTFD: A performances comparison between local and global adaptive methods","authors":"Y. M. Chee, A. Sha'ameri","doi":"10.1109/ICSIPA.2013.6708023","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708023","url":null,"abstract":"The quadratic time-frequency distribution (TFD) provides distribution of energy over the time-frequency plane for time-varying signals. Since phase information is not represented, the cross TFD (XTFD) is proposed to analyze phase shift keying (PSK) signals by providing localized phase information. However, the phase estimation does not yield desirable performances as the time-frequency representation is interfered by duplicated terms. The problem is solved by the proposed XTFD which uses an adaptive window to remove the duplicated term. The local and global adaptive algorithms are proposed to estimate the window width. It is shown that both algorithms meet the theoretical limit at a minimum signal-to-noise ratio (SNR) of 5dB. At lower SNR, the local adaptive method outperforms the global adaptive method at the expense of higher number of computation.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130113137","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6708031
Z. Abidin, M. Manaf, A. S. Shibghatullah
This study focus on compression in wavelet decomposition for security in biometric data. The objectives of this research are two folds: a) to investigate whether compressed human eye image differ with the original eye and b) to obtain the compression ratio values using proposed methods. The experiments have been conducted to explore the application of sparsity-norm balance and sparsity-norm balance square root techniques in wavelet decomposition. The eye image with [320x280] dimension is used through the wavelet 2D tool of Matlab. The results showed that, the percentage of coefficients before compression energy was 99.65% and number of zeros were 97.99%. However, the percentage of energy was 99.97%, increased while the number of zeros was same after compression. Based on our findings, the impact of the compression produces different ratio and with minimal lost after the compression. The future work should imply in artificial intelligent area for protecting biometric data.
{"title":"Experimental approach on thresholding using reverse biorthogonal wavelet decomposition for eye image","authors":"Z. Abidin, M. Manaf, A. S. Shibghatullah","doi":"10.1109/ICSIPA.2013.6708031","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708031","url":null,"abstract":"This study focus on compression in wavelet decomposition for security in biometric data. The objectives of this research are two folds: a) to investigate whether compressed human eye image differ with the original eye and b) to obtain the compression ratio values using proposed methods. The experiments have been conducted to explore the application of sparsity-norm balance and sparsity-norm balance square root techniques in wavelet decomposition. The eye image with [320x280] dimension is used through the wavelet 2D tool of Matlab. The results showed that, the percentage of coefficients before compression energy was 99.65% and number of zeros were 97.99%. However, the percentage of energy was 99.97%, increased while the number of zeros was same after compression. Based on our findings, the impact of the compression produces different ratio and with minimal lost after the compression. The future work should imply in artificial intelligent area for protecting biometric data.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132426410","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 : 1900-01-01DOI: 10.1109/ICSIPA.2013.6707991
A. Nor'aini, R. Sahak, A. Saparon
This paper presents the classification of vagina and pelvis from iris region based on iridology chart using Principal Component Analysis (PCA) and Support Vector Machine with Radial Basis Function kernel (SVM-RBF). The Circular Boundary Detector (CBD) has been introduced for localizing the iris region. This method is able to localize and segment the iris with 100% accuracy. The segmented iris was unwrapped into polar form and cropped into regions of vagina and pelvis based on iridology chart. Features obtained from the cropped regions are extracted using Principle Components Analysis (PCA) and are the inputs to SVM-RBF. Classification accuracy is computed through the comparison of each test feature vector with the target vectors. This study provides the foundation for the development of diagnostic system to monitor the health condition of human body parts.
{"title":"Classification of iris regions using Principal Component Analysis and Support Vector Machine","authors":"A. Nor'aini, R. Sahak, A. Saparon","doi":"10.1109/ICSIPA.2013.6707991","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707991","url":null,"abstract":"This paper presents the classification of vagina and pelvis from iris region based on iridology chart using Principal Component Analysis (PCA) and Support Vector Machine with Radial Basis Function kernel (SVM-RBF). The Circular Boundary Detector (CBD) has been introduced for localizing the iris region. This method is able to localize and segment the iris with 100% accuracy. The segmented iris was unwrapped into polar form and cropped into regions of vagina and pelvis based on iridology chart. Features obtained from the cropped regions are extracted using Principle Components Analysis (PCA) and are the inputs to SVM-RBF. Classification accuracy is computed through the comparison of each test feature vector with the target vectors. This study provides the foundation for the development of diagnostic system to monitor the health condition of human body parts.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292387","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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