Pub Date : 2012-08-31DOI: 10.1109/CBMS.2012.6266365
C. Marrocco, M. Molinara, F. Tortorella, P. Rinaldi, L. Bonomo, Alfredo Ferrarotti, C. Aragno, Salvatore Schiano lo Moriello
The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.
{"title":"Detection of cluster of microcalcifications based on watershed segmentation algorithm","authors":"C. Marrocco, M. Molinara, F. Tortorella, P. Rinaldi, L. Bonomo, Alfredo Ferrarotti, C. Aragno, Salvatore Schiano lo Moriello","doi":"10.1109/CBMS.2012.6266365","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266365","url":null,"abstract":"The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116169307","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266362
G. Gallo, A. Torrisi
Wireless Capsule Endoscopy is a commonly used diagnostic technique to explore intestinal regions which are difficult to reach with traditional endoscopy. The large number of images produced by this technology requires the use of computer-aided tools to select only meaningful frames to speed up the analysis time by the expert. This paper proposes a methodology to identify in an ensemble of WCE frames the images that clearly show the narrowing of the intestinal lumen. The proposed technique uses a custom set of Haar features extracted from the images. These are used for the growth of different binary decision trees. Each tree assigns a label. One image is eventually associated with the class that has the majority vote in the forest. Experiments conducted on real WCE images have proved the effectiveness of the proposal and are reported and discussed.
{"title":"Random forests based WCE frames classification","authors":"G. Gallo, A. Torrisi","doi":"10.1109/CBMS.2012.6266362","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266362","url":null,"abstract":"Wireless Capsule Endoscopy is a commonly used diagnostic technique to explore intestinal regions which are difficult to reach with traditional endoscopy. The large number of images produced by this technology requires the use of computer-aided tools to select only meaningful frames to speed up the analysis time by the expert. This paper proposes a methodology to identify in an ensemble of WCE frames the images that clearly show the narrowing of the intestinal lumen. The proposed technique uses a custom set of Haar features extracted from the images. These are used for the growth of different binary decision trees. Each tree assigns a label. One image is eventually associated with the class that has the majority vote in the forest. Experiments conducted on real WCE images have proved the effectiveness of the proposal and are reported and discussed.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123328155","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266357
Jussi Kinnunen, H. Kokkonen, V. Kovanen, M. Hauta-Kasari, P. Vahimaa, J. Jurvelin
Excessive cross-linking of collagen during aging may contribute to degeneration of articular cartilage. Traditionally, the amount of cross-links is derived by using destructive high-performance liquid chromatography (HPLC). However, a sensitive, nondestructive method could help to evaluate tissue integrity, including cross-links. We increased collagen cross-linking in bovine articular cartilage by controlled threose incubation. During the incubation, optical spectral reflectance images of the samples were captured and related to HPLC results using regression analysis. Significant correlations with reflectance and cross-links were observed. When further developed the optical method may enable nondestructive evaluation of cross-links in vivo through a clinical arthroscope.
{"title":"Changes in collagen cross-linking of articular cartilage are revealed by spectral reflectance imaging","authors":"Jussi Kinnunen, H. Kokkonen, V. Kovanen, M. Hauta-Kasari, P. Vahimaa, J. Jurvelin","doi":"10.1109/CBMS.2012.6266357","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266357","url":null,"abstract":"Excessive cross-linking of collagen during aging may contribute to degeneration of articular cartilage. Traditionally, the amount of cross-links is derived by using destructive high-performance liquid chromatography (HPLC). However, a sensitive, nondestructive method could help to evaluate tissue integrity, including cross-links. We increased collagen cross-linking in bovine articular cartilage by controlled threose incubation. During the incubation, optical spectral reflectance images of the samples were captured and related to HPLC results using regression analysis. Significant correlations with reflectance and cross-links were observed. When further developed the optical method may enable nondestructive evaluation of cross-links in vivo through a clinical arthroscope.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125373952","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266316
K. Drechsler, Marius Erdt, C. O. Laura, S. Wesarg
In this work, we present a system based on open-source toolkits that can utilize multiple phases of a liver CT dataset to plan a surgery before the patient enters the operation room. The contributions are an optimized deformable registration of arterial and venous phases of the liver and a methodology to assess surgical risk utilizing both phases. It is shown by the example of two clinical cases, how our system enables the fusion of complementary information into a 3D representation of the patient anatomy. Processing times of single processing steps on a modern machine are quite low, which allows for an integration into clinical routine.
{"title":"Multiphase risk assessment of atypical liver resections","authors":"K. Drechsler, Marius Erdt, C. O. Laura, S. Wesarg","doi":"10.1109/CBMS.2012.6266316","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266316","url":null,"abstract":"In this work, we present a system based on open-source toolkits that can utilize multiple phases of a liver CT dataset to plan a surgery before the patient enters the operation room. The contributions are an optimized deformable registration of arterial and venous phases of the liver and a methodology to assess surgical risk utilizing both phases. It is shown by the example of two clinical cases, how our system enables the fusion of complementary information into a 3D representation of the patient anatomy. Processing times of single processing steps on a modern machine are quite low, which allows for an integration into clinical routine.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129799818","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266361
M. Baker, A. Kerr, E. Hames, Kwaku Akrofi
Pattern recognition techniques can be applied to problems in medicine to aid diagnostic accuracy and uncover patterns associated with disease states that are not always obvious to the clinician. In this work, a sequential forward floating search technique (SFFS) was applied to the problem of classification of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls. The technique resulted in superior classification rates over statistical methods, as described in the paper. The advantage of SFFS may lie in the technique's ability to identify subgroups within diagnostic categories, and to correctly select features that identify those sub-groups.
{"title":"An SFFS technique for EEG feature classification to identify sub-groups","authors":"M. Baker, A. Kerr, E. Hames, Kwaku Akrofi","doi":"10.1109/CBMS.2012.6266361","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266361","url":null,"abstract":"Pattern recognition techniques can be applied to problems in medicine to aid diagnostic accuracy and uncover patterns associated with disease states that are not always obvious to the clinician. In this work, a sequential forward floating search technique (SFFS) was applied to the problem of classification of patients with Alzheimer's disease (AD), mild cognitive impairment (MCI) and normal controls. The technique resulted in superior classification rates over statistical methods, as described in the paper. The advantage of SFFS may lie in the technique's ability to identify subgroups within diagnostic categories, and to correctly select features that identify those sub-groups.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574003","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266350
Brianna C. Abbey, A. Alipour, Logan Gilmour, Christopher Camp, C. Hofer, R. Lederer, Greig Rasmussen, Lili Liu, I. Nikolaidis, Eleni Stroulia, C. Sadowski
Medication adherence is an important challenge for many patients with chronic conditions, most of them elderly. Technology has an important role to play in this area potentially, with electronic devices equipped with reminder capability and medication intake recording. In this paper, we present a remotely programmable pillbox. This pillbox is equipped with a web application which gives the health professional or caregiver a tool to check and program the pillbox. Also, a mobile application is implemented to establish a connection with the web-application to show pills' daily schedule and pill taking notifications.
{"title":"A remotely programmable smart pillbox for enhancing medication adherence","authors":"Brianna C. Abbey, A. Alipour, Logan Gilmour, Christopher Camp, C. Hofer, R. Lederer, Greig Rasmussen, Lili Liu, I. Nikolaidis, Eleni Stroulia, C. Sadowski","doi":"10.1109/CBMS.2012.6266350","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266350","url":null,"abstract":"Medication adherence is an important challenge for many patients with chronic conditions, most of them elderly. Technology has an important role to play in this area potentially, with electronic devices equipped with reminder capability and medication intake recording. In this paper, we present a remotely programmable pillbox. This pillbox is equipped with a web application which gives the health professional or caregiver a tool to check and program the pillbox. Also, a mobile application is implemented to establish a connection with the web-application to show pills' daily schedule and pill taking notifications.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130085304","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266391
L. Moss, D. Corsar, I. Piper
Vast amounts of medical data are now routinely collected. This data is often subsequently used in medical research. However, the quality of the data can vary widely. Existing automated approaches to data quality assurance largely rely on threshold rules that can miss errors requiring complex domain knowledge to identify. In this paper we describe a framework to assess the reliability of medical data using linked data and semantic web technologies. This approach has been evaluated in the Neuro-Intensive Care Unit domain, successfully identifying potential errors in the recorded observations, and indicating that various ontologies proposed by the medical and sensor network communities can be used to represent medical observation data.
{"title":"A linked data approach to assessing medical data","authors":"L. Moss, D. Corsar, I. Piper","doi":"10.1109/CBMS.2012.6266391","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266391","url":null,"abstract":"Vast amounts of medical data are now routinely collected. This data is often subsequently used in medical research. However, the quality of the data can vary widely. Existing automated approaches to data quality assurance largely rely on threshold rules that can miss errors requiring complex domain knowledge to identify. In this paper we describe a framework to assess the reliability of medical data using linked data and semantic web technologies. This approach has been evaluated in the Neuro-Intensive Care Unit domain, successfully identifying potential errors in the recorded observations, and indicating that various ontologies proposed by the medical and sensor network communities can be used to represent medical observation data.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134003527","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266300
Ruida Cheng, Justin Senseney, Nishidh Pandya, Evan S. McCreedy, M. McAuliffe, Alexandra Bokinsky
A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The framework takes advantage of the parallel processing power on modern graphics hardware with novel OpenCL and GLSL shading language implementations. The Java and GPU environment provide portable advanced biomedical image visualization applications. Several applications built on top of the GPU framework are also presented to show the extensibility of the application. These include multi-surface rendering, stereoscopic rendering, image fusion, and diffusion tensor visualization.
{"title":"A flexible Java GPU-enhanced visualization framework and its applications","authors":"Ruida Cheng, Justin Senseney, Nishidh Pandya, Evan S. McCreedy, M. McAuliffe, Alexandra Bokinsky","doi":"10.1109/CBMS.2012.6266300","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266300","url":null,"abstract":"A flexible biomedical visualization framework implemented with Java, OpenGL, and OpenCL performs efficient volume rendering with large, multi-modal datasets. The framework takes advantage of the parallel processing power on modern graphics hardware with novel OpenCL and GLSL shading language implementations. The Java and GPU environment provide portable advanced biomedical image visualization applications. Several applications built on top of the GPU framework are also presented to show the extensibility of the application. These include multi-surface rendering, stereoscopic rendering, image fusion, and diffusion tensor visualization.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475107","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266344
F. Yin, Jiang Liu, D. Wong, N. Tan, C. Cheung, M. Baskaran, T. Aung, T. Wong
The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.
{"title":"Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis","authors":"F. Yin, Jiang Liu, D. Wong, N. Tan, C. Cheung, M. Baskaran, T. Aung, T. Wong","doi":"10.1109/CBMS.2012.6266344","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266344","url":null,"abstract":"The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131693207","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 : 2012-06-20DOI: 10.1109/CBMS.2012.6266294
Hyokyeong Lee, Rahul Singh
Representation of time series data is a fundamental problem that impacts data analysis in many problems of medicine and life sciences. Due to the inherent high dimensionality of time-series data, dimensionality reduction constitutes one of the important requirements for a successful representation. Piecewise aggregate approximation (PAA) of time series is a widely used approach in this context. In PAA, the time-series is represented as a set of points where each point is defined as the average of the original data points that it represents. Though highly promising, PAA requires estimating the number of segments required for the representation a priori. In this paper we propose a data driven method for optimally and non-parametrically segmenting a timeseries. Instead of depending on a single estimation of the segments, we employ multiple methods and combine the results using an ensemble approach. This allows the proposed approach to be robust for different types of data. With difficult biomedical time series data sets, experimental results show that our method estimated the optimal segments correctly and achieved the highest accuracy in a number of unsupervised time-series classification tasks.
{"title":"Symbolic representation and clustering of bio-medical time-series data using non-parametric segmentation and cluster ensemble","authors":"Hyokyeong Lee, Rahul Singh","doi":"10.1109/CBMS.2012.6266294","DOIUrl":"https://doi.org/10.1109/CBMS.2012.6266294","url":null,"abstract":"Representation of time series data is a fundamental problem that impacts data analysis in many problems of medicine and life sciences. Due to the inherent high dimensionality of time-series data, dimensionality reduction constitutes one of the important requirements for a successful representation. Piecewise aggregate approximation (PAA) of time series is a widely used approach in this context. In PAA, the time-series is represented as a set of points where each point is defined as the average of the original data points that it represents. Though highly promising, PAA requires estimating the number of segments required for the representation a priori. In this paper we propose a data driven method for optimally and non-parametrically segmenting a timeseries. Instead of depending on a single estimation of the segments, we employ multiple methods and combine the results using an ensemble approach. This allows the proposed approach to be robust for different types of data. With difficult biomedical time series data sets, experimental results show that our method estimated the optimal segments correctly and achieved the highest accuracy in a number of unsupervised time-series classification tasks.","PeriodicalId":187652,"journal":{"name":"2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115544608","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：