The diagnosis of many autoimmune diseases can be greatly facilitated by automatic staining patterns classification of Human Epithelial-2 (HEp-2) cells within indirect immunofluorescence (IIF) images. In this paper, we propose a framework to classify the HEp-2 cells by utilizing the deep convolutional neural networks (CNNs). With carefully designed network architecture and optimized parameters, our networks extract features from raw pixels of cell images in a hierarchical manner and perform classification jointly, avoiding using hand-crafted features to represent a HEp-2 cell image. We evaluate our method on the training dataset of HEp-2 cells classification competition held by ICPR 2014. Our system achieves mean class accuracy of 96.7% on the held-out test set and it also obtains competitive performance on the ICPR 2012 cell dataset.
{"title":"HEp-2 Cell Image Classification with Convolutional Neural Networks","authors":"Zhimin Gao, Jianjia Zhang, Luping Zhou, Lei Wang","doi":"10.1109/I3A.2014.15","DOIUrl":"https://doi.org/10.1109/I3A.2014.15","url":null,"abstract":"The diagnosis of many autoimmune diseases can be greatly facilitated by automatic staining patterns classification of Human Epithelial-2 (HEp-2) cells within indirect immunofluorescence (IIF) images. In this paper, we propose a framework to classify the HEp-2 cells by utilizing the deep convolutional neural networks (CNNs). With carefully designed network architecture and optimized parameters, our networks extract features from raw pixels of cell images in a hierarchical manner and perform classification jointly, avoiding using hand-crafted features to represent a HEp-2 cell image. We evaluate our method on the training dataset of HEp-2 cells classification competition held by ICPR 2014. Our system achieves mean class accuracy of 96.7% on the held-out test set and it also obtains competitive performance on the ICPR 2012 cell dataset.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116501014","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}
Ilias Theodorakopoulos, Dimitris Kastaniotis, G. Economou, S. Fotopoulos
A system for automatic classification of staining patterns in IIF imaging is presented. A full pipeline of pre-processing, feature extraction and classification stages is designed in order to overcome specific challenges posed by the nature of the data. In the preprocessing stage the images are subjected to normalization and de-noising using a sparse representation-based technique. A set morphological features, extracted using multi-level thresholding, is combined with a bundle of local gradient descriptors, selected so as to encode textural and structural information of the fluorescent patterns in multiple scales. The proposed method was evaluated using a dataset with over 10K images achieving over 90 percent of classification accuracy.
{"title":"HEp-2 Cells Classification Using Morphological Features and a Bundle of Local Gradient Descriptors","authors":"Ilias Theodorakopoulos, Dimitris Kastaniotis, G. Economou, S. Fotopoulos","doi":"10.1109/I3A.2014.16","DOIUrl":"https://doi.org/10.1109/I3A.2014.16","url":null,"abstract":"A system for automatic classification of staining patterns in IIF imaging is presented. A full pipeline of pre-processing, feature extraction and classification stages is designed in order to overcome specific challenges posed by the nature of the data. In the preprocessing stage the images are subjected to normalization and de-noising using a sparse representation-based technique. A set morphological features, extracted using multi-level thresholding, is combined with a bundle of local gradient descriptors, selected so as to encode textural and structural information of the fluorescent patterns in multiple scales. The proposed method was evaluated using a dataset with over 10K images achieving over 90 percent of classification accuracy.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123663806","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}
Hironori Shigeta, T. Mashita, Takeshi Kaneko, J. Kikuta, S. Seno, H. Takemura, H. Matsuda, M. Ishii
Bio-imaging techniques have recently gotten a lot of attention since they have enabled in-vivo imaging. They are expected to contribute to drug discovery, understanding of disease mechanisms etc. However, data retrieved by bioimaging techniques have been increasing in volume, and it is not anymore feasible to analyze it manually. Therefore automatic extraction of characteristic of a huge amount of data have become important. Moreover, quantitative analysis methods are required for statistical reliability. In this paper we introduce a method for the analysis of a sequence of bone tissue images taken by a two-photon microscope to quantify blood permeability of bone marrow. This method segments the input image sequence to blood vessel, bone marrow and bone regions by graph cuts which we extended according to the images. Permeability is quantified by the intensity of the segmentation result. We also confirm that our method shows that quantification tendency is similar to ground truth data made by an expert.
{"title":"A Graph Cuts Image Segmentation Method for Quantifying Barrier Permeation in Bone Tissue","authors":"Hironori Shigeta, T. Mashita, Takeshi Kaneko, J. Kikuta, S. Seno, H. Takemura, H. Matsuda, M. Ishii","doi":"10.1109/I3A.2014.22","DOIUrl":"https://doi.org/10.1109/I3A.2014.22","url":null,"abstract":"Bio-imaging techniques have recently gotten a lot of attention since they have enabled in-vivo imaging. They are expected to contribute to drug discovery, understanding of disease mechanisms etc. However, data retrieved by bioimaging techniques have been increasing in volume, and it is not anymore feasible to analyze it manually. Therefore automatic extraction of characteristic of a huge amount of data have become important. Moreover, quantitative analysis methods are required for statistical reliability. In this paper we introduce a method for the analysis of a sequence of bone tissue images taken by a two-photon microscope to quantify blood permeability of bone marrow. This method segments the input image sequence to blood vessel, bone marrow and bone regions by graph cuts which we extended according to the images. Permeability is quantified by the intensity of the segmentation result. We also confirm that our method shows that quantification tendency is similar to ground truth data made by an expert.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129065300","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 : 2014-08-24DOI: 10.1109/I3A.WORKSHOP.2014.16
Cascio Donato, Taormina Vincenzo, Cipolla Marco, Fauci Francesco, Vasile Simone Maria, Raso Giuseppe
We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing, features extraction and classification. The choice of methods, features and parameters was performed automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on two steps: the first step follows the one-against-all (OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO. Leave-one-out image cross validation method was used for the evaluation of the results.
{"title":"HEp-2 Cell Classification with Heterogeneous Classes-Processes Based on K-Nearest Neighbours","authors":"Cascio Donato, Taormina Vincenzo, Cipolla Marco, Fauci Francesco, Vasile Simone Maria, Raso Giuseppe","doi":"10.1109/I3A.WORKSHOP.2014.16","DOIUrl":"https://doi.org/10.1109/I3A.WORKSHOP.2014.16","url":null,"abstract":"We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing, features extraction and classification. The choice of methods, features and parameters was performed automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based on two steps: the first step follows the one-against-all (OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO. Leave-one-out image cross validation method was used for the evaluation of the results.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"244 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132146892","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}
Krati Gupta, Vibha Gupta, A. Sao, A. Bhavsar, A. D. Dileep
We propose and analyze a novel framework for classification of HEp-2 cell images. It is based upon two important aspects. First, we propose to utilize the expert knowledge about the visual characteristics of classes to formulate class-specific image features. Secondly, realizing that the problem involves a small number of classes, we treat the classification task as hierarchical verification subtasks. Thus, the overall classification problem is posed as a verification of each class, using its class-specific features. The current study reports the results using the Nuclear Membrane and Golgi classes. We demonstrate that our framework yields high classification rate with simple and efficient feature definitions, and only (20%) of the data for training. We also analyze important aspects such as comparison with non-hierarchical approach, and performance on low-contrast images which are important for early disease detection.
{"title":"Class-Specific Hierarchical Classification of HEp-2 Cell Images: The Case of Two Classes","authors":"Krati Gupta, Vibha Gupta, A. Sao, A. Bhavsar, A. D. Dileep","doi":"10.1109/I3A.2014.13","DOIUrl":"https://doi.org/10.1109/I3A.2014.13","url":null,"abstract":"We propose and analyze a novel framework for classification of HEp-2 cell images. It is based upon two important aspects. First, we propose to utilize the expert knowledge about the visual characteristics of classes to formulate class-specific image features. Secondly, realizing that the problem involves a small number of classes, we treat the classification task as hierarchical verification subtasks. Thus, the overall classification problem is posed as a verification of each class, using its class-specific features. The current study reports the results using the Nuclear Membrane and Golgi classes. We demonstrate that our framework yields high classification rate with simple and efficient feature definitions, and only (20%) of the data for training. We also analyze important aspects such as comparison with non-hierarchical approach, and performance on low-contrast images which are important for early disease detection.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"19 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115030717","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 : 2014-08-24DOI: 10.1109/I3A.WORKSHOP.2014.11
Shahab Ensafi, Shijian Lu, A. Kassim, C. Tan
In this work we present an automatic HEp-2 cell image classification technique that exploits different spatial scaled image representation and sparse coding of SIFT and SURF features. The proposed method is applied on the ICIP2013 dataset in the I3A workshop, which is held in ICPR 2014 conference. Experiments are designed to capture the accuracies on training set with cross validation method. Additionally, the prior information on positive and intensity levels of cells are used to boost the overall performance. Finally, different number of iterations on learning the dictionary is studied to find the optimum one.
{"title":"A Bag of Words Based Approach for Classification of HEp-2 Cell Images","authors":"Shahab Ensafi, Shijian Lu, A. Kassim, C. Tan","doi":"10.1109/I3A.WORKSHOP.2014.11","DOIUrl":"https://doi.org/10.1109/I3A.WORKSHOP.2014.11","url":null,"abstract":"In this work we present an automatic HEp-2 cell image classification technique that exploits different spatial scaled image representation and sparse coding of SIFT and SURF features. The proposed method is applied on the ICIP2013 dataset in the I3A workshop, which is held in ICPR 2014 conference. Experiments are designed to capture the accuracies on training set with cross validation method. Additionally, the prior information on positive and intensity levels of cells are used to boost the overall performance. Finally, different number of iterations on learning the dictionary is studied to find the optimum one.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756280","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 : 2014-08-24DOI: 10.1109/I3A.WORKSHOP.2014.18
Diego Gragnaniello, Carlo Sansone, L. Verdoliva
This work deals with the design of a classification method for cells extracted from Indirect Immunofluorescence images. In particular, we propose to use a dense local descriptor invariant both to scale changes and to rotations in order to classify the six categories of staining patterns of the cells. The descriptor is able to give a compact and discriminative representation and combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag of Words is finally used to perform classification and experimental results show very good performance.
本工作涉及设计一种从间接免疫荧光图像中提取细胞的分类方法。特别是，我们建议使用密集的局部描述符不变量来缩放变化和旋转，以便对细胞的六种染色模式进行分类。该描述符能够给出紧凑的判别表示，并将对数极坐标采样与空间变化的高斯平滑相结合，应用于特定方向的梯度图像。最后使用Bag of Words进行分类，实验结果显示了很好的分类效果。
{"title":"Biologically-Inspired Dense Local Descriptor for Indirect Immunofluorescence Image Classification","authors":"Diego Gragnaniello, Carlo Sansone, L. Verdoliva","doi":"10.1109/I3A.WORKSHOP.2014.18","DOIUrl":"https://doi.org/10.1109/I3A.WORKSHOP.2014.18","url":null,"abstract":"This work deals with the design of a classification method for cells extracted from Indirect Immunofluorescence images. In particular, we propose to use a dense local descriptor invariant both to scale changes and to rotations in order to classify the six categories of staining patterns of the cells. The descriptor is able to give a compact and discriminative representation and combines a log-polar sampling with spatially-varying gaussian smoothing applied on the gradients images in specific directions. Bag of Words is finally used to perform classification and experimental results show very good performance.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124272877","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}
This paper describes our texture descriptor ensemble aimed to compete for the Cell Level classification task (Task 1) in the "Contest on Performance Evaluation on Indirect Immunofluorescence Image Analysis Systems", hosted by the I3A Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. Our system is based on the combination of 4 descriptors based on Local Binary Pattern (LBP) and 1 morphological feature set: the multiscale Pyramid LBP, Local Configuration Pattern, Rotation Invariant Co-occurrence among adjacent LBP, Extended LBP and finally Strandmark morphological features. From each image a total of 2643 features are extracted. The corresponding 5 feature sets are classified using Support Vector Machines and results are combined according to the sum rule. By using a 10-fold cross validation testing protocol, the proposed ensemble obtains 60.9% of accuracy, outperforming many state-of-art stand-alone texture descriptors as well as other ensembles.
{"title":"Morphological and Texture Features for HEp-2 Cells Classification","authors":"L. Nanni, M. Paci, F. C. Santos, J. Hyttinen","doi":"10.1109/I3A.2014.11","DOIUrl":"https://doi.org/10.1109/I3A.2014.11","url":null,"abstract":"This paper describes our texture descriptor ensemble aimed to compete for the Cell Level classification task (Task 1) in the \"Contest on Performance Evaluation on Indirect Immunofluorescence Image Analysis Systems\", hosted by the I3A Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images. Our system is based on the combination of 4 descriptors based on Local Binary Pattern (LBP) and 1 morphological feature set: the multiscale Pyramid LBP, Local Configuration Pattern, Rotation Invariant Co-occurrence among adjacent LBP, Extended LBP and finally Strandmark morphological features. From each image a total of 2643 features are extracted. The corresponding 5 feature sets are classified using Support Vector Machines and results are combined according to the sum rule. By using a 10-fold cross validation testing protocol, the proposed ensemble obtains 60.9% of accuracy, outperforming many state-of-art stand-alone texture descriptors as well as other ensembles.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129250009","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}
T. Mashita, Jun Usam, Hironori Shigeta, Yoshihiro Kuroda, J. Kikuta, S. Seno, M. Ishii, H. Matsuda, H. Takemura
The improvement of bioimaging technologies enables the observation of cellular dynamics invivo. Some new bioimaging technologies are expected to contribute to the discovery of new drugs and mechanisms of disease. To improve the contributions of bioimaging, it is required to extract a particular region or to detect a particular cell's motion within bioimages. Moreover, automatic extraction and detection with image processing is also required because the accurate and uniformed processing of a massive number of images manually is unrealistic. To help automate this process, we introduce a bone marrow cavity segmentation method for two-photon excitation microscopy images. Specialists of cellular dynamics define regions of bone marrow cavity by considering several criteria, including characteristics of intensity and blood flow. We take those criteria into our method as the energy function of graph cuts. Results of evaluations and comparison with normal graph cuts show that our proposed method that does not use hard constraints achieved a performance better than normal graph cuts with hard constraints.
{"title":"A Segmentation Method for Bone Marrow Cavity Imaging Using Graph Cuts","authors":"T. Mashita, Jun Usam, Hironori Shigeta, Yoshihiro Kuroda, J. Kikuta, S. Seno, M. Ishii, H. Matsuda, H. Takemura","doi":"10.1109/I3A.2014.21","DOIUrl":"https://doi.org/10.1109/I3A.2014.21","url":null,"abstract":"The improvement of bioimaging technologies enables the observation of cellular dynamics invivo. Some new bioimaging technologies are expected to contribute to the discovery of new drugs and mechanisms of disease. To improve the contributions of bioimaging, it is required to extract a particular region or to detect a particular cell's motion within bioimages. Moreover, automatic extraction and detection with image processing is also required because the accurate and uniformed processing of a massive number of images manually is unrealistic. To help automate this process, we introduce a bone marrow cavity segmentation method for two-photon excitation microscopy images. Specialists of cellular dynamics define regions of bone marrow cavity by considering several criteria, including characteristics of intensity and blood flow. We take those criteria into our method as the energy function of graph cuts. Results of evaluations and comparison with normal graph cuts show that our proposed method that does not use hard constraints achieved a performance better than normal graph cuts with hard constraints.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114555809","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}
The I3A2014 contest participants had to design and implement a pattern recognition system able to classify the cells belonging to HEp-2 images in one of six pattern classes. We propose the QR-FIRMLP architecture, an extension of the finite impulse response multilayer perceptron (FIRMLP). The FIRMLP is a multilayer perceptron where the static weights have been replaced by finite impulse response filters. We have extended this architecture by replacing some sigmoidal layers with quadratic ones and adding recurrent connections to the FIR neurons.
{"title":"Quadratic Recurrent Finite Impulse Response MLP for Indirect Immunofluorescence Image Recognition","authors":"Cristinel Codrescu","doi":"10.1109/I3A.2014.14","DOIUrl":"https://doi.org/10.1109/I3A.2014.14","url":null,"abstract":"The I3A2014 contest participants had to design and implement a pattern recognition system able to classify the cells belonging to HEp-2 images in one of six pattern classes. We propose the QR-FIRMLP architecture, an extension of the finite impulse response multilayer perceptron (FIRMLP). The FIRMLP is a multilayer perceptron where the static weights have been replaced by finite impulse response filters. We have extended this architecture by replacing some sigmoidal layers with quadratic ones and adding recurrent connections to the FIR neurons.","PeriodicalId":103785,"journal":{"name":"2014 1st Workshop on Pattern Recognition Techniques for Indirect Immunofluorescence Images","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130684042","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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