Ching-Fen Jiang, De-Kai Chen, Yuan-Si Li, Jheng-Long Kuo
The proportion of senile people tends to increase in the global population. The cognitive impairment in the three dimensional (3D) space is usually associated with senile dementia. This paper presents a computer-aid tool developed to evaluate and train the cognitive ability in the 3D space by using the virtual reality technique. Once the norm is developed, this tool can assist the physiotherapist to evaluate the patients' 3D cognitive function in a more objective and efficient way. The viability of 3D cognitive training will be evaluated among the normal subjects prior to clinical trials
{"title":"Development of a Computer-Aided Tool for Evaluation and Training in 3D Spatial Cognitive Function","authors":"Ching-Fen Jiang, De-Kai Chen, Yuan-Si Li, Jheng-Long Kuo","doi":"10.1109/CBMS.2006.75","DOIUrl":"https://doi.org/10.1109/CBMS.2006.75","url":null,"abstract":"The proportion of senile people tends to increase in the global population. The cognitive impairment in the three dimensional (3D) space is usually associated with senile dementia. This paper presents a computer-aid tool developed to evaluate and train the cognitive ability in the 3D space by using the virtual reality technique. Once the norm is developed, this tool can assist the physiotherapist to evaluate the patients' 3D cognitive function in a more objective and efficient way. The viability of 3D cognitive training will be evaluated among the normal subjects prior to clinical trials","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"2006 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127637697","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 comparison operators available in traditional database management systems (DBMS) are not adequate to handle complex data such as images, rather comparing them using similarity operators is the option of choice. Similarity operators need a way to measure the similarity between pairs of objects. Although there are many interesting works dealing with similarity queries and functions to measure similarity, they all rely on a single similarity function that must be applicable over the whole dataset. However, images from medical exams often require several ways to measure similarity, depending on many factors, such as the particular pathological condition being searched, or the existence of specific clinical condition revealed in the images compared. Therefore, the ability to handle several ways to compare images by similarity is important in medical software handling images. This work develop a technique to allow several similarity functions to be combined when indexing a large set of images, allowing queries to probe the dataset regarding distinct comparison criteria. This technique also allows a flexible way to pose queries supporting fast retrieval of the answers
{"title":"Distance Functions Association for Content-Based Image Retrieval using Multiple Comparison Criteria","authors":"I. Pola, A. Traina, C. Traina","doi":"10.1109/CBMS.2006.78","DOIUrl":"https://doi.org/10.1109/CBMS.2006.78","url":null,"abstract":"The comparison operators available in traditional database management systems (DBMS) are not adequate to handle complex data such as images, rather comparing them using similarity operators is the option of choice. Similarity operators need a way to measure the similarity between pairs of objects. Although there are many interesting works dealing with similarity queries and functions to measure similarity, they all rely on a single similarity function that must be applicable over the whole dataset. However, images from medical exams often require several ways to measure similarity, depending on many factors, such as the particular pathological condition being searched, or the existence of specific clinical condition revealed in the images compared. Therefore, the ability to handle several ways to compare images by similarity is important in medical software handling images. This work develop a technique to allow several similarity functions to be combined when indexing a large set of images, allowing queries to probe the dataset regarding distinct comparison criteria. This technique also allows a flexible way to pose queries supporting fast retrieval of the answers","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128024276","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}
In this paper we investigate if techniques based on decision trees are also useful to classify ischemia. In order to do that, we have based on a previous own algorithm that is able of detecting ST-segment episodes, can be executed in real time and is lightweight enough to be implemented on mobile devices such as PDAs. Three main steps are performed by that algorithm: 1) signal preprocessing that extracts important features from the ECG signal 2) detection of suspect ST segment events and 3) rejection of irrelevant ST segment events. We have found that techniques based on decision trees are interesting for the last step in order to obtain a set of rules that reject some of the suspect ST segment events detected in the second step. The freely available part of the LTST database has been used to develop the detector and the rest of the same database has been used for validation purposes. The sensitivity of the detector over all the records of the LTST database is 89.89% for episodes annotated according to C protocol and the positive predictivity is 70.03% for episodes annotated according to A protocol. Those results improve our previously obtained ones and we think that they are comparable to other results that appear in the literature. However, it has to be noticed that we have not found works that detect ischemia in real time and show validation results over the LTST database
{"title":"Using DecisionTrees for Real-Time Ischemia Detection","authors":"L. Dranca, A. Goñi, A. Illarramendi","doi":"10.1109/CBMS.2006.163","DOIUrl":"https://doi.org/10.1109/CBMS.2006.163","url":null,"abstract":"In this paper we investigate if techniques based on decision trees are also useful to classify ischemia. In order to do that, we have based on a previous own algorithm that is able of detecting ST-segment episodes, can be executed in real time and is lightweight enough to be implemented on mobile devices such as PDAs. Three main steps are performed by that algorithm: 1) signal preprocessing that extracts important features from the ECG signal 2) detection of suspect ST segment events and 3) rejection of irrelevant ST segment events. We have found that techniques based on decision trees are interesting for the last step in order to obtain a set of rules that reject some of the suspect ST segment events detected in the second step. The freely available part of the LTST database has been used to develop the detector and the rest of the same database has been used for validation purposes. The sensitivity of the detector over all the records of the LTST database is 89.89% for episodes annotated according to C protocol and the positive predictivity is 70.03% for episodes annotated according to A protocol. Those results improve our previously obtained ones and we think that they are comparable to other results that appear in the literature. However, it has to be noticed that we have not found works that detect ischemia in real time and show validation results over the LTST database","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114982174","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}
Tzu-Hsiang Yang, Po-Hsun Cheng, C. H. Yang, F. Lai, C. L. Chen, Hsiu-Hui Lee, K. Hsu, Chi-Huang Chen, Ching-Ting Tan, Yeali S. Sun
This article describes the successful experiences of National Taiwan University Hospital (NTUH) in moving from IBM Mainframe to connected networking computer systems. We use multi-tier architecture and HL7 standard to implement our new outpatient hospital information system (HIS). The NTUH HIS is a complex environment with several operating systems, databases, and information systems. We adopt service-oriented architecture (SOA) to reduce the complex relations between systems and solve data consistency problems among databases. We also show that the distributed architecture can provide us stable and reasonable system performances. Our main contribution is proving that the distributed environment with HL7 standard and SOA can sustain in a highly demanding environment
{"title":"A Scalable Multi-tier Architecture for the National Taiwan University Hospital Information System based on HL7 Standard","authors":"Tzu-Hsiang Yang, Po-Hsun Cheng, C. H. Yang, F. Lai, C. L. Chen, Hsiu-Hui Lee, K. Hsu, Chi-Huang Chen, Ching-Ting Tan, Yeali S. Sun","doi":"10.1109/CBMS.2006.27","DOIUrl":"https://doi.org/10.1109/CBMS.2006.27","url":null,"abstract":"This article describes the successful experiences of National Taiwan University Hospital (NTUH) in moving from IBM Mainframe to connected networking computer systems. We use multi-tier architecture and HL7 standard to implement our new outpatient hospital information system (HIS). The NTUH HIS is a complex environment with several operating systems, databases, and information systems. We adopt service-oriented architecture (SOA) to reduce the complex relations between systems and solve data consistency problems among databases. We also show that the distributed architecture can provide us stable and reasonable system performances. Our main contribution is proving that the distributed environment with HL7 standard and SOA can sustain in a highly demanding environment","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115355425","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 general goal of this work is modeling and reconstruction of a three-dimensional (3D) rat from a series of two-dimensional (2D) images captured from an inexpensive digital camera. We proposed a hybrid segmentation method that incorporates symmetric region grow (symRG) and active contour modeling (ACM) to robustly extract regions of interest (ROIs), such as organs, spines, and vessels. symRG is employed to enhance the segmentation performance while the edge information passed from the ACM can help prevent over-segmentation. We built a component-based software platform that includes the symRG and ACM components as well as the other image enhancement, post-segmentation processing, surface rendering components allowing the user to dynamically compose a streamlined 3D rat reconstruction procedure or script. The example dataset in this paper include 284 slices of 2D rat whole-body images. Separate scripts were used to model and visualize the body, heart, lung, stomach, and head. Few user-imposed parameters were required and the whole processing , from loading series of 2D images towards 3D rendition to demonstrate the results, is within two minutes
{"title":"Synergy of Symmetric Region Grow and Active Contour in Reconstruction of a 3D Rat","authors":"Shu-Yen Wan, Chian-Hung Hou","doi":"10.1109/CBMS.2006.153","DOIUrl":"https://doi.org/10.1109/CBMS.2006.153","url":null,"abstract":"The general goal of this work is modeling and reconstruction of a three-dimensional (3D) rat from a series of two-dimensional (2D) images captured from an inexpensive digital camera. We proposed a hybrid segmentation method that incorporates symmetric region grow (symRG) and active contour modeling (ACM) to robustly extract regions of interest (ROIs), such as organs, spines, and vessels. symRG is employed to enhance the segmentation performance while the edge information passed from the ACM can help prevent over-segmentation. We built a component-based software platform that includes the symRG and ACM components as well as the other image enhancement, post-segmentation processing, surface rendering components allowing the user to dynamically compose a streamlined 3D rat reconstruction procedure or script. The example dataset in this paper include 284 slices of 2D rat whole-body images. Separate scripts were used to model and visualize the body, heart, lung, stomach, and head. Few user-imposed parameters were required and the whole processing , from loading series of 2D images towards 3D rendition to demonstrate the results, is within two minutes","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124246490","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}
Chien-Cheng Lee, Sz-Han Chen, H. Tsai, P. Chung, Yu-Chun Chiang
In this paper, a liver disease diagnosis based on Gabor filters is proposed. Three kinds of liver diseases are identified: cyst, hepatoma and cavernous hemangioma. The diagnosis scheme includes two steps: features extraction and classification. The features derived from Gabor filters are obtained from the ROIs among the normal and abnormal CT images. In the classification step the SVM classifier is used to discriminate the different liver disease types. Finally the receiver operating characteristic curve is employed to evaluate the performance of the diagnosis system. The effectiveness of the proposed method is demonstrated through experimental results on CT images including 76 liver cysts, 30 hepatomas, and 40 cavernous hemangiomas. From the results, we can observe that the discrimination rate of cyst is higher than the other diseases, and the classification accuracy decreases slightly between cavernous hemangiomas and hepatomas. However, a normal region can be discriminated from all of these diseases entirely
{"title":"Discrimination of Liver Diseases from CT Images Based on Gabor Filters","authors":"Chien-Cheng Lee, Sz-Han Chen, H. Tsai, P. Chung, Yu-Chun Chiang","doi":"10.1109/CBMS.2006.77","DOIUrl":"https://doi.org/10.1109/CBMS.2006.77","url":null,"abstract":"In this paper, a liver disease diagnosis based on Gabor filters is proposed. Three kinds of liver diseases are identified: cyst, hepatoma and cavernous hemangioma. The diagnosis scheme includes two steps: features extraction and classification. The features derived from Gabor filters are obtained from the ROIs among the normal and abnormal CT images. In the classification step the SVM classifier is used to discriminate the different liver disease types. Finally the receiver operating characteristic curve is employed to evaluate the performance of the diagnosis system. The effectiveness of the proposed method is demonstrated through experimental results on CT images including 76 liver cysts, 30 hepatomas, and 40 cavernous hemangiomas. From the results, we can observe that the discrimination rate of cyst is higher than the other diseases, and the classification accuracy decreases slightly between cavernous hemangiomas and hepatomas. However, a normal region can be discriminated from all of these diseases entirely","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794876","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}
Radial basis function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. One problem of classical RBF is that it suffers from curse of dimensionality that the number of basis functions would explode with the increase of dimensions in the dataset. This explosion usually impairs the usefulness and interpretability of RBF, especially in medical applications, where the dimensions of dataset are high and the explanations of solutions are important. In this paper, we propose a generalized RBF (GRBF) model to reduce the number of basis functions and thus alleviate curse of dimensionality. An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. This would make the learning process simpler and more convenient in practice. Moreover, GRBF trained by the new algorithm has an apparent statistical meaning. Experimental results show potentials for real-life applications
{"title":"A Modified Generalized RBF Model with EM-based Learning Algorithm for Medical Applications","authors":"Li Ma, Abdul Wahab, Hiok-Chai Quek","doi":"10.1109/CBMS.2006.17","DOIUrl":"https://doi.org/10.1109/CBMS.2006.17","url":null,"abstract":"Radial basis function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. One problem of classical RBF is that it suffers from curse of dimensionality that the number of basis functions would explode with the increase of dimensions in the dataset. This explosion usually impairs the usefulness and interpretability of RBF, especially in medical applications, where the dimensions of dataset are high and the explanations of solutions are important. In this paper, we propose a generalized RBF (GRBF) model to reduce the number of basis functions and thus alleviate curse of dimensionality. An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. This would make the learning process simpler and more convenient in practice. Moreover, GRBF trained by the new algorithm has an apparent statistical meaning. Experimental results show potentials for real-life applications","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124949235","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}
Document clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), bisecting K-means, K-means, and suffix tree clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as bisecting K-means, K-means and STC, gains some benefit from MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology
{"title":"Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study","authors":"Illhoi Yoo, Xiaohua Hu","doi":"10.1109/CBMS.2006.62","DOIUrl":"https://doi.org/10.1109/CBMS.2006.62","url":null,"abstract":"Document clustering has been used for better document retrieval, document browsing, and text mining. In this paper, we investigate if biomedical ontology MeSH improves the clustering quality for MEDLINE articles. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods (single-link, complete-link, and complete link), bisecting K-means, K-means, and suffix tree clustering (STC) in terms of efficiency, effectiveness, and scalability. According to our experiment results, biomedical ontology MeSH significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as bisecting K-means, K-means and STC, gains some benefit from MeSH ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of MeSH ontology","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125089295","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}
F. Prados, A. Bardera, M. Sbert, I. Boada, M. Feixas
Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach
{"title":"A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI","authors":"F. Prados, A. Bardera, M. Sbert, I. Boada, M. Feixas","doi":"10.1109/CBMS.2006.20","DOIUrl":"https://doi.org/10.1109/CBMS.2006.20","url":null,"abstract":"Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124650456","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}
K. Bliley, D. J. Schwab, D. Holmes, P. H. Kane, J. Levine, E. Daniel, B. Gilbert
Interest in studying human posture, movement, and physical activity is growing due in part to the increasing prevalence of obesity. Accelerometers are commonly used in motion analysis systems to enable researchers to conduct studies outside of the traditional laboratory environment; however the available systems tend to be bulky and unsuitable for long-term studies. Therefore, a need exists for a physically robust, yet compact motion analysis system that can be easily worn for an extended time period without disrupting the person's range of motion. Here we describe our on-going efforts to develop a robust, compact system that can measure body posture and movement using a tri-axial accelerometer, and then store this data on a secure digital memory card. This device can be easily configured to collect accelerometer data for specific applications in human motion analysis. In the future, this device will be used to study physical activity in free-living individuals
{"title":"Design of a Compact System Using a MEMS Accelerometer to Measure Body Posture and Ambulation","authors":"K. Bliley, D. J. Schwab, D. Holmes, P. H. Kane, J. Levine, E. Daniel, B. Gilbert","doi":"10.1109/CBMS.2006.73","DOIUrl":"https://doi.org/10.1109/CBMS.2006.73","url":null,"abstract":"Interest in studying human posture, movement, and physical activity is growing due in part to the increasing prevalence of obesity. Accelerometers are commonly used in motion analysis systems to enable researchers to conduct studies outside of the traditional laboratory environment; however the available systems tend to be bulky and unsuitable for long-term studies. Therefore, a need exists for a physically robust, yet compact motion analysis system that can be easily worn for an extended time period without disrupting the person's range of motion. Here we describe our on-going efforts to develop a robust, compact system that can measure body posture and movement using a tri-axial accelerometer, and then store this data on a secure digital memory card. This device can be easily configured to collect accelerometer data for specific applications in human motion analysis. In the future, this device will be used to study physical activity in free-living individuals","PeriodicalId":208693,"journal":{"name":"19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125221624","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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