Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Precisely selecting the regions that contain masses is an important step in mass segmentation using mammographic computer-aided detection. In this paper, an algorithm for extracting mass regions in digital mammograms is proposed, in which we use adaptive histogram equalization to enhance mammograms, use a gradient vector flow field to generate region boundaries, select N candidate locations according to the means and the standard deviations of intensities of the points with top brightness, use these points and the region boundaries to generate the convex hulls of the regions as the mass regions. 161 down-sampled mammogram images from the Digital Database for Screening Mammography project were test, and a detection rate of 82.6% is obtained. The experimental results indicated that the method is efficient and robust.
{"title":"Gradient Vector Flow Field and Mass Region Extraction in Digital Mammograms","authors":"F. Zou, Yufeng Zheng, Zhengdong Zhou, K. Agyepong","doi":"10.1109/CBMS.2008.117","DOIUrl":"https://doi.org/10.1109/CBMS.2008.117","url":null,"abstract":"Mass detection is one of the main computer-aided mammographic breast cancer detection techniques. Precisely selecting the regions that contain masses is an important step in mass segmentation using mammographic computer-aided detection. In this paper, an algorithm for extracting mass regions in digital mammograms is proposed, in which we use adaptive histogram equalization to enhance mammograms, use a gradient vector flow field to generate region boundaries, select N candidate locations according to the means and the standard deviations of intensities of the points with top brightness, use these points and the region boundaries to generate the convex hulls of the regions as the mass regions. 161 down-sampled mammogram images from the Digital Database for Screening Mammography project were test, and a detection rate of 82.6% is obtained. The experimental results indicated that the method is efficient and robust.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127177711","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}
Mapping concepts from medical terminologies, such as the UMLS, to medical documents is a prerequisite for many tasks of (automatically) processing documents. Due to the nature of the UMLS and the tools to accomplish the mapping, it is not always possible to achieve a correct and unambiguous mapping. This drawback led us to the development of an editor for correcting the obtained information. Our editor, MapFace, visualizes information received by the MetaMap Transfer (MMTx) program and enables users to edit and correct this information on both a conceptual and a syntactical level. By means of this functionality we are able to provide further processing steps with correct and appropriate input. Furthermore, the visualization features enable users to validate or even generate hypotheses, as well as support the better understanding of medical text.
将概念从医学术语(如UMLS)映射到医学文档是许多(自动)处理文档任务的先决条件。由于UMLS的性质和完成映射的工具，实现正确和明确的映射并不总是可能的。这一缺点促使我们开发了一种编辑器，用于纠正所获得的信息。我们的编辑器MapFace将MetaMap Transfer (MMTx)程序接收到的信息可视化，并使用户能够在概念和语法层面上编辑和纠正这些信息。通过此功能，我们能够提供具有正确和适当输入的进一步处理步骤。此外，可视化功能使用户能够验证甚至生成假设，并支持更好地理解医学文本。
{"title":"MapFace - An Editor for MetaMap Transfer (MMTx)","authors":"K. Kaiser, T. Gschwandtner, P. Martini","doi":"10.1109/CBMS.2008.116","DOIUrl":"https://doi.org/10.1109/CBMS.2008.116","url":null,"abstract":"Mapping concepts from medical terminologies, such as the UMLS, to medical documents is a prerequisite for many tasks of (automatically) processing documents. Due to the nature of the UMLS and the tools to accomplish the mapping, it is not always possible to achieve a correct and unambiguous mapping. This drawback led us to the development of an editor for correcting the obtained information. Our editor, MapFace, visualizes information received by the MetaMap Transfer (MMTx) program and enables users to edit and correct this information on both a conceptual and a syntactical level. By means of this functionality we are able to provide further processing steps with correct and appropriate input. Furthermore, the visualization features enable users to validate or even generate hypotheses, as well as support the better understanding of medical text.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"38 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114148257","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}
We present an ontology based approach to identify hypertensive patients who show non-adherence to prescribed medication. Using the Web ontology language (OWL), we have developed an ontology that includes patient prescription details, medication possession ratios (MPRs) and blood pressure measurements (together with other patient related information) that has been populated with production electronic medical record (EMR) data from a general medical practice in New Zealand. We have written queries using the semantic query-enhanced Web rule language (SQWRL) to query this ontology to determine patients who have lapsed medication while having a low MPR. We also discuss some practical issues related to patient recall based on EMR data, as well as the suitability of the proposed scheme.
{"title":"A Semantic Web Technology Based Approach to Identify Hypertensive Patients for Follow-Up/Recall","authors":"T. Mabotuwana, J. Warren","doi":"10.1109/CBMS.2008.12","DOIUrl":"https://doi.org/10.1109/CBMS.2008.12","url":null,"abstract":"We present an ontology based approach to identify hypertensive patients who show non-adherence to prescribed medication. Using the Web ontology language (OWL), we have developed an ontology that includes patient prescription details, medication possession ratios (MPRs) and blood pressure measurements (together with other patient related information) that has been populated with production electronic medical record (EMR) data from a general medical practice in New Zealand. We have written queries using the semantic query-enhanced Web rule language (SQWRL) to query this ontology to determine patients who have lapsed medication while having a low MPR. We also discuss some practical issues related to patient recall based on EMR data, as well as the suitability of the proposed scheme.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122201991","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}
Revlin Abbi, E. El-Darzi, C. Vasilakis, P. Millard
In this paper we propose a new approach capable of determining clinically meaningful patient groups from a given dataset of patient spells. We hypothesise that the skewed distribution of length of stay (LOS) observations, often modelled in the past using mixed exponential equations, is composed of several homogeneous groups that together form the overall skewed LOS distribution. We show how the Gaussian mixture model (GMM) can be used to approximate each group, and discuss each group's possible clinical interpretation and statistical significance. In addition, we show how the health professional can use the outcome of the grouping approach to answer several questions about individual patients and their likely LOS in hospital. Our results demonstrate that the grouping of stroke patient spells estimated by the GMM resembles the clinical experience of stroke patients and the different stroke recovery patterns.
{"title":"A Gaussian Mixture Model Approach to Grouping Patients According to their Hospital Length of Stay","authors":"Revlin Abbi, E. El-Darzi, C. Vasilakis, P. Millard","doi":"10.1109/CBMS.2008.69","DOIUrl":"https://doi.org/10.1109/CBMS.2008.69","url":null,"abstract":"In this paper we propose a new approach capable of determining clinically meaningful patient groups from a given dataset of patient spells. We hypothesise that the skewed distribution of length of stay (LOS) observations, often modelled in the past using mixed exponential equations, is composed of several homogeneous groups that together form the overall skewed LOS distribution. We show how the Gaussian mixture model (GMM) can be used to approximate each group, and discuss each group's possible clinical interpretation and statistical significance. In addition, we show how the health professional can use the outcome of the grouping approach to answer several questions about individual patients and their likely LOS in hospital. Our results demonstrate that the grouping of stroke patient spells estimated by the GMM resembles the clinical experience of stroke patients and the different stroke recovery patterns.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122276134","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}
Many medical diagnosis applications are characterized by datasets that contain under- represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naive Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naive Bayesian. Finally, we give an outlook for further work.
{"title":"Evaluating a Case-Based Classifier for Biomedical Applications","authors":"S. Little, O. Salvetti, P. Perner","doi":"10.1109/CBMS.2008.87","DOIUrl":"https://doi.org/10.1109/CBMS.2008.87","url":null,"abstract":"Many medical diagnosis applications are characterized by datasets that contain under- represented classes due to the fact that the disease appears more rarely than the normal case. In such a situation classifiers that generalize over the data such as decision trees and Naive Bayesian are not the proper choice as classification methods. Case-based classifiers that can work on the samples seen so far are more appropriate for such a task. We propose to calculate the contingency table and class specific evaluation measures despite the overall accuracy for evaluation purposes of classifiers for these specific data characteristics. We evaluate the different options of our case-based classifier and compare the performance to decision trees and Naive Bayesian. Finally, we give an outlook for further work.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128538375","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}
Ontologies and controlled vocabularies are being established by many groups to provide roadmaps through the confused mass of data currently being generated from increasingly large-scale experimental biological experiments. The world of protein chemistry is no exception to this rule, with protein ontology (PO) having lead the field by providing a framework in which individual molecules and complexes can be defined by their structure, function and cellular location. PO performs searches across the protein databases using a standard nomenclature consistent to all entries.
{"title":"Use of Protein Ontology to Enable Data Exchange for Complex Proteomic Experiments","authors":"A. Sidhu, T. Dillon, E. Chang","doi":"10.1109/CBMS.2008.145","DOIUrl":"https://doi.org/10.1109/CBMS.2008.145","url":null,"abstract":"Ontologies and controlled vocabularies are being established by many groups to provide roadmaps through the confused mass of data currently being generated from increasingly large-scale experimental biological experiments. The world of protein chemistry is no exception to this rule, with protein ontology (PO) having lead the field by providing a framework in which individual molecules and complexes can be defined by their structure, function and cellular location. PO performs searches across the protein databases using a standard nomenclature consistent to all entries.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129031706","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 main aim of this paper is to model the neonatal unit of a perinatal network centre using the general framework of a loss network model and to estimate some performance measures. A special case of the class of model has been applied for capacity planning to the perinatal network centre of a neonatal network in the United Kingdom. Using the data supplied from the perinatal network centre about admission process, length of stay (LoS) and discharge pattern of the babies, the loss network model is applied to estimate the admission refusal probability in the system under steady-state conditions. Results are derived for different arrival patterns and different combinations of cots at all levels of care of the neonatal unit. This approach can be useful to select the optimal combination of cots for any given acceptance rate of arrival to the neonatal unit.
{"title":"Modelling and Performance Measure of a Perinatal Network Centre in the United Kingdom","authors":"M. Asaduzzaman, T. Chaussalet","doi":"10.1109/CBMS.2008.50","DOIUrl":"https://doi.org/10.1109/CBMS.2008.50","url":null,"abstract":"The main aim of this paper is to model the neonatal unit of a perinatal network centre using the general framework of a loss network model and to estimate some performance measures. A special case of the class of model has been applied for capacity planning to the perinatal network centre of a neonatal network in the United Kingdom. Using the data supplied from the perinatal network centre about admission process, length of stay (LoS) and discharge pattern of the babies, the loss network model is applied to estimate the admission refusal probability in the system under steady-state conditions. Results are derived for different arrival patterns and different combinations of cots at all levels of care of the neonatal unit. This approach can be useful to select the optimal combination of cots for any given acceptance rate of arrival to the neonatal unit.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117143739","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 proposed method serves for detecting and analysing translating and non-translating periodic motion with small oscillation from video data. Periodic motion in a video sequence is detected using principles of motion accumulation. Non-translating periodic motion is described as change of the point brightness over the time. The trajectory of the motion is obtained by tracking the translating object with periodic motion. Following frequency analysis is done by the means of Fourier transform. The developed method is applied for observing patients suffering from Parkinson disease.
{"title":"Periodic Motion Detection on Patient with Motion Disorders","authors":"Zdenka Uhríková, Václav Hlaváč","doi":"10.1109/CBMS.2008.97","DOIUrl":"https://doi.org/10.1109/CBMS.2008.97","url":null,"abstract":"The proposed method serves for detecting and analysing translating and non-translating periodic motion with small oscillation from video data. Periodic motion in a video sequence is detected using principles of motion accumulation. Non-translating periodic motion is described as change of the point brightness over the time. The trajectory of the motion is obtained by tracking the translating object with periodic motion. Following frequency analysis is done by the means of Fourier transform. The developed method is applied for observing patients suffering from Parkinson disease.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371589","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 paper presents the multimedia component included in TESYS, a medical e-learning platform that is used nowadays at the University of Medicine and Pharmacy Craiova at disciplines like gastroenterology and urology in order to complete the traditional medical learning process. The multimedia component contains a database with color medical images acquired by the teachers from different patients in the diagnosis process, so represent real cases. A series of alphanumerical information: diagnosis, treatment and patient evolution are added for each image. The multimedia component together with modern search methods: content-based image query and content-based region query is used both in the training process and e-testing one. The content-based visual query uses characteristics that are automatically extracted from medical images (color, texture, regions). Using content-based visual query with other access methods (text-based, hierarchical methods) on a teaching image database allows students to see images and associated information from database in a simple and direct manner. This method stimulates learning, by comparing similar cases along with their particularities, or by comparing cases that are visually similar, but with different diagnoses.
{"title":"Imagistic Database for Medical e-Learning","authors":"L. Stanescu, D. Burdescu, A. Ion, A. Panus","doi":"10.1109/CBMS.2008.38","DOIUrl":"https://doi.org/10.1109/CBMS.2008.38","url":null,"abstract":"The paper presents the multimedia component included in TESYS, a medical e-learning platform that is used nowadays at the University of Medicine and Pharmacy Craiova at disciplines like gastroenterology and urology in order to complete the traditional medical learning process. The multimedia component contains a database with color medical images acquired by the teachers from different patients in the diagnosis process, so represent real cases. A series of alphanumerical information: diagnosis, treatment and patient evolution are added for each image. The multimedia component together with modern search methods: content-based image query and content-based region query is used both in the training process and e-testing one. The content-based visual query uses characteristics that are automatically extracted from medical images (color, texture, regions). Using content-based visual query with other access methods (text-based, hierarchical methods) on a teaching image database allows students to see images and associated information from database in a simple and direct manner. This method stimulates learning, by comparing similar cases along with their particularities, or by comparing cases that are visually similar, but with different diagnoses.","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128088897","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}
Clinical guidelines systematically assist practitioners with providing appropriate health care for specific clinical circumstances. However, a significant number of guidelines are lacking in quality. In this paper, we use the UML modeling language to capture guidelines and model checking techniques for their verification. We have established a classification of possible properties to be verified in a guideline and we present an automated approach based on a translation from UML to PROMELA, the input language of the SPIN model checker. Our approach is illustrated with a guideline based on a guideline published by the National Guideline Clearing House (NGC).
{"title":"Verification of Clinical Guidelines by Model Checking","authors":"B. Pérez, Ivan Porres","doi":"10.1109/CBMS.2008.86","DOIUrl":"https://doi.org/10.1109/CBMS.2008.86","url":null,"abstract":"Clinical guidelines systematically assist practitioners with providing appropriate health care for specific clinical circumstances. However, a significant number of guidelines are lacking in quality. In this paper, we use the UML modeling language to capture guidelines and model checking techniques for their verification. We have established a classification of possible properties to be verified in a guideline and we present an automated approach based on a translation from UML to PROMELA, the input language of the SPIN model checker. Our approach is illustrated with a guideline based on a guideline published by the National Guideline Clearing House (NGC).","PeriodicalId":377855,"journal":{"name":"2008 21st IEEE International Symposium on Computer-Based Medical Systems","volume":"22 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126067792","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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