Pub Date : 2002-06-04DOI: 10.1109/CBMS.2002.1011398
Solomon Atnafu, R. Chbeir, L. Brunie
The need for systems that can store, represent and provide efficient retrieval facilities for images of particular interest is becoming very high in medicine. In this respect, a lot of work has been done to integrate image data in standard data processing environments. The two different approaches that are used for the representation of images are the meta-data and the content-based approaches. Users in medicine need queries that use both content-based and meta-data representations of images or salient objects. In this paper, we first present a global image data model that supports both meta-data and low-level descriptions of images and their salient objects. This allows us to make multi-criteria image retrieval (context-, semantic- and content-based queries). Then, we present an image data repository model that captures all the data described in the model and permits the integration of heterogeneous operations in a DBMS. In particular, content-based operations (content-based join and selection) in combination with traditional ones can be carried out using our model.
{"title":"Content-based and metadata retrieval in medical image database","authors":"Solomon Atnafu, R. Chbeir, L. Brunie","doi":"10.1109/CBMS.2002.1011398","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011398","url":null,"abstract":"The need for systems that can store, represent and provide efficient retrieval facilities for images of particular interest is becoming very high in medicine. In this respect, a lot of work has been done to integrate image data in standard data processing environments. The two different approaches that are used for the representation of images are the meta-data and the content-based approaches. Users in medicine need queries that use both content-based and meta-data representations of images or salient objects. In this paper, we first present a global image data model that supports both meta-data and low-level descriptions of images and their salient objects. This allows us to make multi-criteria image retrieval (context-, semantic- and content-based queries). Then, we present an image data repository model that captures all the data described in the model and permits the integration of heterogeneous operations in a DBMS. In particular, content-based operations (content-based join and selection) in combination with traditional ones can be carried out using our model.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121795822","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011362
B. Brumen, T. Welzer, M. Druzovec, I. Golob, H. Jaakkola
In the last few decades, medical data has mainly been a by-product of daily operations. In general, not much has been used for analytical purposes, other than reporting and simple statistics. Just recently, it has become clear that data are important assets if used for analyses that help decision-making. To be able to analyse the data, one needs to have full access to the relevant sources. This may contradict one of the paramount requirements - to have secure, private data - especially if the data analyst is outsourced and not directly affiliated with the data owner, as is often the case in medical environments. In this paper, we present data analyses from the data protection point of view. We propose a solution for outsourced model-based data analyses. A formal framework for protecting the data that leaves the organization's boundary, based on the relational data model's abstract data type, is presented. The data and the data structure are modified so that the process of data analysis can still take place and the results can still be obtained, but the data content itself is hard to reveal. Once the data analysis results are returned, the inverse process discloses the meaning of the model to the data owners.
{"title":"Protecting medical data for analyses","authors":"B. Brumen, T. Welzer, M. Druzovec, I. Golob, H. Jaakkola","doi":"10.1109/CBMS.2002.1011362","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011362","url":null,"abstract":"In the last few decades, medical data has mainly been a by-product of daily operations. In general, not much has been used for analytical purposes, other than reporting and simple statistics. Just recently, it has become clear that data are important assets if used for analyses that help decision-making. To be able to analyse the data, one needs to have full access to the relevant sources. This may contradict one of the paramount requirements - to have secure, private data - especially if the data analyst is outsourced and not directly affiliated with the data owner, as is often the case in medical environments. In this paper, we present data analyses from the data protection point of view. We propose a solution for outsourced model-based data analyses. A formal framework for protecting the data that leaves the organization's boundary, based on the relational data model's abstract data type, is presented. The data and the data structure are modified so that the process of data analysis can still take place and the results can still be obtained, but the data content itself is hard to reveal. Once the data analysis results are returned, the inverse process discloses the meaning of the model to the data owners.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116519317","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011384
Herculano de Biasi, A. V. Wangenheim, P. Silveira, E. Comunello
A methodology is presented in order to aid in the surgical planning of abdominal aortic aneurysms. This method addresses image recognition, especially of the aorta and abdominal aneurysms. After the recognition and segmentation of these structures, it is possible to obtain a correct measurement of the same structures, generating more reliable data for manufacturing the required endoprostheses for the treatment of the pathology. With the 3D reconstruction of the anatomical structures, is also possible to make the visualization and analysis of the data more reliable. This work is part of the Cyclops project, which intends to develop intelligent tools for the analysis of medical images, accomplished as a partnership between the Universidade Federal de Santa Catarina (UFSC) and the University of Kaiserslautern, Germany.
{"title":"3D reconstruction of abdominal aortic aneurysms","authors":"Herculano de Biasi, A. V. Wangenheim, P. Silveira, E. Comunello","doi":"10.1109/CBMS.2002.1011384","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011384","url":null,"abstract":"A methodology is presented in order to aid in the surgical planning of abdominal aortic aneurysms. This method addresses image recognition, especially of the aorta and abdominal aneurysms. After the recognition and segmentation of these structures, it is possible to obtain a correct measurement of the same structures, generating more reliable data for manufacturing the required endoprostheses for the treatment of the pathology. With the 3D reconstruction of the anatomical structures, is also possible to make the visualization and analysis of the data more reliable. This work is part of the Cyclops project, which intends to develop intelligent tools for the analysis of medical images, accomplished as a partnership between the Universidade Federal de Santa Catarina (UFSC) and the University of Kaiserslautern, Germany.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124297250","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011395
J. Pereira, A. Martínez, D. Ronda, Bernardino Arcay Varela, A. Pazos
This article describes a system for the acquisition, storage and secure remote access to information generated by a hospital's devices for image diagnosis. The system allows the experts of a medical centre to recover and manage information. Our solution also makes it possible to obtain more elaborate results, thanks to the implemented functionalities of image analysis (edge detectors, clustering algorithms, etc.) and the storage of historical diagnosis results that give a certain experience to the application.
{"title":"Development of a system for access to and exploitation of medical images","authors":"J. Pereira, A. Martínez, D. Ronda, Bernardino Arcay Varela, A. Pazos","doi":"10.1109/CBMS.2002.1011395","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011395","url":null,"abstract":"This article describes a system for the acquisition, storage and secure remote access to information generated by a hospital's devices for image diagnosis. The system allows the experts of a medical centre to recover and manage information. Our solution also makes it possible to obtain more elaborate results, thanks to the implemented functionalities of image analysis (edge detectors, clustering algorithms, etc.) and the storage of historical diagnosis results that give a certain experience to the application.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127827809","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}
Exceptional events in a time series are observations which can be regarded as qualitatively significant anomalies. The detection of such events is an interesting problem in several domains, in particular for the generation of alarms in clinical microbiology. We propose an approach to the detection of exceptional events based on model selection. For each mathematical form of a model, we choose the parameters of the model by maximum likelihood techniques. Then we select, among the resulting instantiated models, the model which minimizes the mean square error. An exceptional event is detected with an assigned probability, if an observation lies outside the forecasting region defined by the selected model and a confidence interval.
{"title":"Detecting outbreaks by time series analysis","authors":"Gianfranco Cellarosi, Stefano Lodi, Claudio Sartori","doi":"10.1109/CBMS.2002.1011371","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011371","url":null,"abstract":"Exceptional events in a time series are observations which can be regarded as qualitatively significant anomalies. The detection of such events is an interesting problem in several domains, in particular for the generation of alarms in clinical microbiology. We propose an approach to the detection of exceptional events based on model selection. For each mathematical form of a model, we choose the parameters of the model by maximum likelihood techniques. Then we select, among the resulting instantiated models, the model which minimizes the mean square error. An exceptional event is detected with an assigned probability, if an observation lies outside the forecasting region defined by the selected model and a confidence interval.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123895686","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011369
Eric Plévin, D. Zazula
Deals with the problem of decomposition of surface electromyograms (SEMG). According to the physiological facts, a multiple-input multiple-output (MIMO) is used. The measured signals are taken as the channel responses corresponding to the motor-unit action potentials (MUAPs) convolution by the innervation pulse trains. The decomposition is based on the third-order cumulants whose values enter as coefficients of nonlinear system of equations. The system is solved by nonlinear least mean square (LMS) optimisation. Synthetic SEMG signals from a MIMO(2,3) with additive Gaussian noise with SNRs of 10 and 0 dB prove that a successful multichannel decomposition is possible also in very noisy environments.
{"title":"Decomposition of surface EMG signals using non-linear LMS optimisation of higher-order cumulants","authors":"Eric Plévin, D. Zazula","doi":"10.1109/CBMS.2002.1011369","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011369","url":null,"abstract":"Deals with the problem of decomposition of surface electromyograms (SEMG). According to the physiological facts, a multiple-input multiple-output (MIMO) is used. The measured signals are taken as the channel responses corresponding to the motor-unit action potentials (MUAPs) convolution by the innervation pulse trains. The decomposition is based on the third-order cumulants whose values enter as coefficients of nonlinear system of equations. The system is solved by nonlinear least mean square (LMS) optimisation. Synthetic SEMG signals from a MIMO(2,3) with additive Gaussian noise with SNRs of 10 and 0 dB prove that a successful multichannel decomposition is possible also in very noisy environments.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447941","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011378
D. Korosec, A. Holobar, M. Divjak, D. Zazula
Medicine is a difficult thing to learn. Experimenting with real patients should not be the only option - simulation deserves special attention. The Virtual Reality Modelling Language (VRML), as a tool to build virtual objects and scenes, has a good record of educational applications in medicine, especially for static and animated visualisations of body parts and organs. However, to create computer simulations resembling situations in real environments, the demanded level of interactivity and dynamics is difficult to achieve. In this paper, we describe some approaches and techniques which we used to push the limits of the current VRML technology further towards a dynamic 3D representation of virtual environments. Our demonstration is based on the implementation of a virtual baby model, whose vital signs can be controlled from an external Java application. The main contribution of this work is a proposal for a modified VRML timer/sensor implementation, which greatly improves the overall control of system performance.
{"title":"Dynamic VRML for simulated training in medicine","authors":"D. Korosec, A. Holobar, M. Divjak, D. Zazula","doi":"10.1109/CBMS.2002.1011378","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011378","url":null,"abstract":"Medicine is a difficult thing to learn. Experimenting with real patients should not be the only option - simulation deserves special attention. The Virtual Reality Modelling Language (VRML), as a tool to build virtual objects and scenes, has a good record of educational applications in medicine, especially for static and animated visualisations of body parts and organs. However, to create computer simulations resembling situations in real environments, the demanded level of interactivity and dynamics is difficult to achieve. In this paper, we describe some approaches and techniques which we used to push the limits of the current VRML technology further towards a dynamic 3D representation of virtual environments. Our demonstration is based on the implementation of a virtual baby model, whose vital signs can be controlled from an external Java application. The main contribution of this work is a proposal for a modified VRML timer/sensor implementation, which greatly improves the overall control of system performance.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982675","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011367
M. Zorman, G. Masuda, P. Kokol, Ryuichi Yamamoto, B. Stiglic
Searching for new rules and new knowledge in problem areas, where very little or almost none previous knowledge is present, can be a very long and demanding process. In our research we addressed the problem of finding new knowledge in the form of rules in the diabetes database using a combination of decision trees and association rules. The first question we wanted to answer was, if there are significant differences in sets of rules both approaches produce, and how rules, produced by decision trees behave, after being a subject of filtering and reduction, normally used in association rule approaches. In order to accomplish that, we had to make some modifications to both the decision tree approach and association rule approach. From the first results we can conclude, that the sets of rules, built by decision trees are much smaller than the sets created by association rules. We could also establish, that filtering and reduction did not effect the rules derived from decision trees in the same scale as association rules.
{"title":"Mining diabetes database with decision trees and association rules","authors":"M. Zorman, G. Masuda, P. Kokol, Ryuichi Yamamoto, B. Stiglic","doi":"10.1109/CBMS.2002.1011367","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011367","url":null,"abstract":"Searching for new rules and new knowledge in problem areas, where very little or almost none previous knowledge is present, can be a very long and demanding process. In our research we addressed the problem of finding new knowledge in the form of rules in the diabetes database using a combination of decision trees and association rules. The first question we wanted to answer was, if there are significant differences in sets of rules both approaches produce, and how rules, produced by decision trees behave, after being a subject of filtering and reduction, normally used in association rule approaches. In order to accomplish that, we had to make some modifications to both the decision tree approach and association rule approach. From the first results we can conclude, that the sets of rules, built by decision trees are much smaller than the sets created by association rules. We could also establish, that filtering and reduction did not effect the rules derived from decision trees in the same scale as association rules.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124854466","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011386
Z. Dokur, M. N. Kurnaz, T. Ölmez
A quantizer neural network (QNN) is proposed for the segmentation of ultrasound images. The elements of the feature vectors are formed by the image intensities within the neighborhood of the pixel of interest. The QNN is a hybrid neural network structure, which is trained by genetic algorithms. The genetic algorithms are used to find optimum values for the weights of the nodes. The hybrid neural network is compared with a multilayer perceptron (MLP) for the segmentation of ultrasound images.
{"title":"Segmentation of ultrasound images by using quantizer neural network","authors":"Z. Dokur, M. N. Kurnaz, T. Ölmez","doi":"10.1109/CBMS.2002.1011386","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011386","url":null,"abstract":"A quantizer neural network (QNN) is proposed for the segmentation of ultrasound images. The elements of the feature vectors are formed by the image intensities within the neighborhood of the pixel of interest. The QNN is a hybrid neural network structure, which is trained by genetic algorithms. The genetic algorithms are used to find optimum values for the weights of the nodes. The hybrid neural network is compared with a multilayer perceptron (MLP) for the segmentation of ultrasound images.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130626834","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 : 2002-06-04DOI: 10.1109/CBMS.2002.1011382
Iryna Skrypnyk
Diagnostics of hearing impairments is a non-trivial problem for data mining techniques. The state of hearing can be described via a measurement of polymorphic disorders in the voice structure that are secondary to restricted auditory control. The diagnostic voice analysis determines voice descriptors that can be used for marginal estimation of the state of hearing. This problem is hard for most of the predictive data mining methods. The presence of strongly correlated and redundant information in the set of voice descriptors might be one reason for the low prediction accuracy. In this paper, different feature selection techniques are evaluated by their ability to raise the prediction accuracy by discarding irrelevant and redundant voice descriptors when modeling the dependency between functional changes within a phonatory organ and restricted auditory control. As the result of the prediction varies for different prediction methods, the applicability of certain feature selection technique is considered with respect to the prediction method and evaluated as a feature selection strategy.
{"title":"Comparison of feature selection strategies for hearing impairments diagnostics","authors":"Iryna Skrypnyk","doi":"10.1109/CBMS.2002.1011382","DOIUrl":"https://doi.org/10.1109/CBMS.2002.1011382","url":null,"abstract":"Diagnostics of hearing impairments is a non-trivial problem for data mining techniques. The state of hearing can be described via a measurement of polymorphic disorders in the voice structure that are secondary to restricted auditory control. The diagnostic voice analysis determines voice descriptors that can be used for marginal estimation of the state of hearing. This problem is hard for most of the predictive data mining methods. The presence of strongly correlated and redundant information in the set of voice descriptors might be one reason for the low prediction accuracy. In this paper, different feature selection techniques are evaluated by their ability to raise the prediction accuracy by discarding irrelevant and redundant voice descriptors when modeling the dependency between functional changes within a phonatory organ and restricted auditory control. As the result of the prediction varies for different prediction methods, the applicability of certain feature selection technique is considered with respect to the prediction method and evaluated as a feature selection strategy.","PeriodicalId":369629,"journal":{"name":"Proceedings of 15th IEEE Symposium on Computer-Based Medical Systems (CBMS 2002)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132880172","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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