Pub Date : 2015-03-11DOI: 10.1109/PRIA.2015.7161649
Afsoon Khodaei, G. Hossein-Zadeh, E. S. Ananloo
As a crucial mental disorder, schizophrenia affects one percent of the world population. Diagnosis of this disorder is now based mainly on the clinical symptoms. As the first study in Iran, we investigate the magnetic resonance imaging (MRI) images of patients with schizophrenia to investigate the imaging biomarkers for helping the diagnosis of this disorder. In this study, we have analyzed MRI images from 12 schizophrenia patients and 12 healthy controls. We have examined the volume of the subcortical brain regions using a fully-automated whole brain segmentation technique. Volumes of these regions were compared between the groups of patient and control. The results showed significant volume reduction in hippocampus, amygdala, thalamus, cerebellum and brain stem between two groups (p-value ≤0.05). Detection of these abnormalities helps us diagnosis of this disorder and hopefully find the appropriate medication for treatment. Also the results of this study are consistent with several reported volumetric differences associated with schizophrenia.
{"title":"Comparison of volumes of subcortical regions in schizophrenia patients and healthy controls using MRI","authors":"Afsoon Khodaei, G. Hossein-Zadeh, E. S. Ananloo","doi":"10.1109/PRIA.2015.7161649","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161649","url":null,"abstract":"As a crucial mental disorder, schizophrenia affects one percent of the world population. Diagnosis of this disorder is now based mainly on the clinical symptoms. As the first study in Iran, we investigate the magnetic resonance imaging (MRI) images of patients with schizophrenia to investigate the imaging biomarkers for helping the diagnosis of this disorder. In this study, we have analyzed MRI images from 12 schizophrenia patients and 12 healthy controls. We have examined the volume of the subcortical brain regions using a fully-automated whole brain segmentation technique. Volumes of these regions were compared between the groups of patient and control. The results showed significant volume reduction in hippocampus, amygdala, thalamus, cerebellum and brain stem between two groups (p-value ≤0.05). Detection of these abnormalities helps us diagnosis of this disorder and hopefully find the appropriate medication for treatment. Also the results of this study are consistent with several reported volumetric differences associated with schizophrenia.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123907848","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161631
B. Moradi, M. Ezoji
In this paper a skin detection method based on the two-dimensional histogram of color images is presented. First, from the statistical perspective, we select the discriminant features in the hope of getting a better average TPR and FPR. Despite the other methods, new statistics of these features are extracted based on the 2-D histograms leading finally to the considering the contextual information. At the last step, the decision is reached based on a feature fusion strategy. The experimental results on the known databases (containing 103 images of humans under uncontrolled condition) demonstrate the performance of the proposed method.
{"title":"Skin detection based on contextual information","authors":"B. Moradi, M. Ezoji","doi":"10.1109/PRIA.2015.7161631","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161631","url":null,"abstract":"In this paper a skin detection method based on the two-dimensional histogram of color images is presented. First, from the statistical perspective, we select the discriminant features in the hope of getting a better average TPR and FPR. Despite the other methods, new statistics of these features are extracted based on the 2-D histograms leading finally to the considering the contextual information. At the last step, the decision is reached based on a feature fusion strategy. The experimental results on the known databases (containing 103 images of humans under uncontrolled condition) demonstrate the performance of the proposed method.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117257959","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161629
Masoud Zadghorban, M. Nahvi
Hand tracking is one of the most important phases of a sign language recognition system that affects the final recognition rate directly. Kalman filter is a well-known technique for object tracking. By minimizing the mean square error, this filter is able to estimate the past, present and future states in a process, even in systems that are inherently uncertain. Hand movement in sign language video is very complex. Hence, Kalman filter is a suitable estimator to predict the hands motion. In this paper, we present an approach to optimize the Kalman filter to track the movement of hands accurately. The modified Kalman filter is then compared with other tracking methods by testing on the Persian sign language video database made by authors.
{"title":"Improving the performance of Kalman filter for hand tracking in Persian sign language video","authors":"Masoud Zadghorban, M. Nahvi","doi":"10.1109/PRIA.2015.7161629","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161629","url":null,"abstract":"Hand tracking is one of the most important phases of a sign language recognition system that affects the final recognition rate directly. Kalman filter is a well-known technique for object tracking. By minimizing the mean square error, this filter is able to estimate the past, present and future states in a process, even in systems that are inherently uncertain. Hand movement in sign language video is very complex. Hence, Kalman filter is a suitable estimator to predict the hands motion. In this paper, we present an approach to optimize the Kalman filter to track the movement of hands accurately. The modified Kalman filter is then compared with other tracking methods by testing on the Persian sign language video database made by authors.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131727983","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161616
M. Rezaeian, G. Hossein-Zadeh, H. Soltanian-Zadeh
Quantitative evaluation of chemical exchange saturation transfer (CEST) is usually done by solving Bloch-McConnell equations (BME). BMEs are not easily extended and applying them to describe the multi-pool data involves a complex process. In this paper, we developed a Gaussian mixture model (GMM) to represent each component involved in the Z-spectrum by a Gaussian distribution. We then tested and evaluated the GMM for the two-pool exchange site and experimental data. The results showed that GMM is able to fit the experimental data and its accuracy is almost similar to that of the BME model. (average percent of Relative Sum Square Error (%RSSE) <;0.6). Accuracy and simplicity were found to be the advantages of the GMM and lack of analytical relationships among the GMM parameters and physical characteristics of the CEST effect turned out to be its main limitations. We quantified contrast agent (CA) concentration (population fraction of CEST pool) and chemical exchange rate applying the GMM to the simulated data of a two-pool exchange site. It was found that the means and variances of the Gaussians can be used for this purpose. In addition, GMM determines the resonance frequency of each pool easily and accurately because these frequencies are equal to the mean values of GMM.
{"title":"Quantification of the CEST effect by Gaussian mixture modeling of Z-spectrum","authors":"M. Rezaeian, G. Hossein-Zadeh, H. Soltanian-Zadeh","doi":"10.1109/PRIA.2015.7161616","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161616","url":null,"abstract":"Quantitative evaluation of chemical exchange saturation transfer (CEST) is usually done by solving Bloch-McConnell equations (BME). BMEs are not easily extended and applying them to describe the multi-pool data involves a complex process. In this paper, we developed a Gaussian mixture model (GMM) to represent each component involved in the Z-spectrum by a Gaussian distribution. We then tested and evaluated the GMM for the two-pool exchange site and experimental data. The results showed that GMM is able to fit the experimental data and its accuracy is almost similar to that of the BME model. (average percent of Relative Sum Square Error (%RSSE) <;0.6). Accuracy and simplicity were found to be the advantages of the GMM and lack of analytical relationships among the GMM parameters and physical characteristics of the CEST effect turned out to be its main limitations. We quantified contrast agent (CA) concentration (population fraction of CEST pool) and chemical exchange rate applying the GMM to the simulated data of a two-pool exchange site. It was found that the means and variances of the Gaussians can be used for this purpose. In addition, GMM determines the resonance frequency of each pool easily and accurately because these frequencies are equal to the mean values of GMM.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133878633","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161637
Iman Iraei, K. Faez
This paper propose an algorithm that uses Mean Shift and Kalman Filter for object tracking. Also this method uses Edge Histogram for occlusion handling. Firstly, we use Mean Shift algorithm to obtain center of desired object. But the robust of tracking is not very well, so we use Kalman Filter to improve the effect of tracking. Bhattacharyya coefficient and Edge Histogram are used for finding out both partial and full occlusions. With this approach we can track the object more accurately. The results prove that the robust of tracking is very well.
{"title":"Object tracking with occlusion handling using mean shift, Kalman filter and Edge Histogram","authors":"Iman Iraei, K. Faez","doi":"10.1109/PRIA.2015.7161637","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161637","url":null,"abstract":"This paper propose an algorithm that uses Mean Shift and Kalman Filter for object tracking. Also this method uses Edge Histogram for occlusion handling. Firstly, we use Mean Shift algorithm to obtain center of desired object. But the robust of tracking is not very well, so we use Kalman Filter to improve the effect of tracking. Bhattacharyya coefficient and Edge Histogram are used for finding out both partial and full occlusions. With this approach we can track the object more accurately. The results prove that the robust of tracking is very well.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965965","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161625
F. Hoseinkhani, B. Nasersharif
Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminative transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Most of discriminative feature transformation measures don't consider the classification method errors and information. In this paper, we propose a feature transformation method for support vector machine to consider both features discrimination and classification error. To this end, we use Multi-Objective Particle Swarm Optimization (Multi-PSO), where we consider two mentioned criteria as objectives in Multi-PSO fitness function. Experimental results on UCI dataset show that the proposed Multi-PSO based feature transformation method outperform other conventional methods of feature transformation when it is used as a preprocessing step for SVM.
{"title":"A feature transformation method based on multi objective particle swarm optimization for reducing support vector machine error","authors":"F. Hoseinkhani, B. Nasersharif","doi":"10.1109/PRIA.2015.7161625","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161625","url":null,"abstract":"Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminative transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Most of discriminative feature transformation measures don't consider the classification method errors and information. In this paper, we propose a feature transformation method for support vector machine to consider both features discrimination and classification error. To this end, we use Multi-Objective Particle Swarm Optimization (Multi-PSO), where we consider two mentioned criteria as objectives in Multi-PSO fitness function. Experimental results on UCI dataset show that the proposed Multi-PSO based feature transformation method outperform other conventional methods of feature transformation when it is used as a preprocessing step for SVM.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133479662","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161630
Hamidreza Hosseinzadeh, F. Razzazi
Learning handwriting categories fail to perform well when trained and tested on data from different databases. In this paper, we propose a novel framework of Ensemble Projection (EP) for writer adaptation. We employed EP as a feature transformation method which can be combined with different types of classifiers for unsupervised and semi-supervised adaptation. Experiments on a handwritten digit dataset demonstrate that EP learning can increase recognition rates significantly, both in the unsupervised and semi-supervised cases.
{"title":"A writer adaptation method for isolated handwritten digit recognition based on Ensemble Projection of features","authors":"Hamidreza Hosseinzadeh, F. Razzazi","doi":"10.1109/PRIA.2015.7161630","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161630","url":null,"abstract":"Learning handwriting categories fail to perform well when trained and tested on data from different databases. In this paper, we propose a novel framework of Ensemble Projection (EP) for writer adaptation. We employed EP as a feature transformation method which can be combined with different types of classifiers for unsupervised and semi-supervised adaptation. Experiments on a handwritten digit dataset demonstrate that EP learning can increase recognition rates significantly, both in the unsupervised and semi-supervised cases.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131896479","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161639
A. Rashno, S. Ahadi, M. Kelarestaghi
Automatic speaker verification (ASV) systems usually use high dimension feature vectors and therefore involve high complexity. However, many of the features used in such systems are believed to be irrelevant and redundant. So far, many wrapper-based methods for feature dimension reduction in these systems have been proposed. Meanwhile, the complexity of such methods is high since system performance is used for feature subset evaluation. In this paper, we propose a new feature selection approach for ASV systems based on ant colony optimization(ACO) and support vector machine (SVM) classifiers which uses feature relief weights in order to have a lower number of feature subset evaluation. This method has led to 64% feature dimension reduction with a 1.745% Equal Error Rate (EER) for the best case appeared in polynomial kernel of SVM. The proposed method has also been compared with Genetic Algorithm (GA) regarding feature selection task. Results indicate that the EER and the number of selected features for the proposed method are lower for different kernels of SVM.
{"title":"Text-independent speaker verification with ant colony optimization feature selection and support vector machine","authors":"A. Rashno, S. Ahadi, M. Kelarestaghi","doi":"10.1109/PRIA.2015.7161639","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161639","url":null,"abstract":"Automatic speaker verification (ASV) systems usually use high dimension feature vectors and therefore involve high complexity. However, many of the features used in such systems are believed to be irrelevant and redundant. So far, many wrapper-based methods for feature dimension reduction in these systems have been proposed. Meanwhile, the complexity of such methods is high since system performance is used for feature subset evaluation. In this paper, we propose a new feature selection approach for ASV systems based on ant colony optimization(ACO) and support vector machine (SVM) classifiers which uses feature relief weights in order to have a lower number of feature subset evaluation. This method has led to 64% feature dimension reduction with a 1.745% Equal Error Rate (EER) for the best case appeared in polynomial kernel of SVM. The proposed method has also been compared with Genetic Algorithm (GA) regarding feature selection task. Results indicate that the EER and the number of selected features for the proposed method are lower for different kernels of SVM.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"374 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115567471","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161640
Fahimeh Sadat Saleh, R. Azmi
Skin lesion segmentation is one of the most important steps for automated early skin cancer detection since the accuracy of the following steps significantly depends on it. In this paper we present a novel approach based on spectral clustering that provides accurate and effective segmentation for dermoscopy images. In the proposed method, an optimized clustering algorithm has been provided which effectively extracts lesion borders using spectral graph partitioning algorithm in an appropriate color space, considering special characteristics of dermoscopy images. The proposed segmentation method has been applied to 170 dermoscopic images and evaluated with two metrics, by means of the segmentation results provided by an experienced dermatologist as the ground truth. The experiment results of this approach demonstrate that, complex contours are distinguished correctly while challenging features of skin lesions such as topological changes, weak or false contours, and asymmetry in color and shape are handled as might be expected when compared to four state of the art methods.
{"title":"Automated lesion border detection of dermoscopy images using spectral clustering","authors":"Fahimeh Sadat Saleh, R. Azmi","doi":"10.1109/PRIA.2015.7161640","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161640","url":null,"abstract":"Skin lesion segmentation is one of the most important steps for automated early skin cancer detection since the accuracy of the following steps significantly depends on it. In this paper we present a novel approach based on spectral clustering that provides accurate and effective segmentation for dermoscopy images. In the proposed method, an optimized clustering algorithm has been provided which effectively extracts lesion borders using spectral graph partitioning algorithm in an appropriate color space, considering special characteristics of dermoscopy images. The proposed segmentation method has been applied to 170 dermoscopic images and evaluated with two metrics, by means of the segmentation results provided by an experienced dermatologist as the ground truth. The experiment results of this approach demonstrate that, complex contours are distinguished correctly while challenging features of skin lesions such as topological changes, weak or false contours, and asymmetry in color and shape are handled as might be expected when compared to four state of the art methods.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126040271","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 : 2015-03-11DOI: 10.1109/PRIA.2015.7161645
Maryam Moradi, Azin Falahati, A. Shahbahrami, Reza Zare-Hassanpour
Wireless Capsule Endoscopy (WCE) is a noninvasive device for detection of gastrointestinal problems especially small bowel diseases, such as polyps which causes gastrointestinal bleeding. The quality of WCE images is very important for diagnosis. In this paper, a new method is proposed to improve the quality of WCE images. In our proposed method for improving the quality of WCE images, Removing Noise and Contrast Enhancement (RNCE) algorithm is used. The algorithm have been implemented and tested on some real images. Quality metrics used for performance evaluation of the proposed method is Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Edge Strength Similarity for Image (ESSIM). The results obtained from SSIM, PSNR and ESSIM indicate that the implemented RNCE method improve the quality of WCE images significantly.
{"title":"Improving visual quality in wireless capsule endoscopy images with contrast-limited adaptive histogram equalization","authors":"Maryam Moradi, Azin Falahati, A. Shahbahrami, Reza Zare-Hassanpour","doi":"10.1109/PRIA.2015.7161645","DOIUrl":"https://doi.org/10.1109/PRIA.2015.7161645","url":null,"abstract":"Wireless Capsule Endoscopy (WCE) is a noninvasive device for detection of gastrointestinal problems especially small bowel diseases, such as polyps which causes gastrointestinal bleeding. The quality of WCE images is very important for diagnosis. In this paper, a new method is proposed to improve the quality of WCE images. In our proposed method for improving the quality of WCE images, Removing Noise and Contrast Enhancement (RNCE) algorithm is used. The algorithm have been implemented and tested on some real images. Quality metrics used for performance evaluation of the proposed method is Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR) and Edge Strength Similarity for Image (ESSIM). The results obtained from SSIM, PSNR and ESSIM indicate that the implemented RNCE method improve the quality of WCE images significantly.","PeriodicalId":163817,"journal":{"name":"2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131300298","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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