The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform

B. B. Wein - One of the best experts on this subject based on the ideXlab platform.

  • Content-based image retrieval in medical applications for picture archiving and communication systems
    Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, 2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, B. B. Wein
    Abstract:

    Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access Transparency for data, methods, and experiments, (b) replication Transparency for methods in development, (c) Concurrency Transparency for job processing and feature extraction, (d) system Transparency at method implementation time, and (e) job distribution Transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

  • The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications
    2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, Klaus Spitzer, Hermann Ney, B. B. Wein
    Abstract:

    The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). Medical expert knowledge is incorporated into several layers. In the registered data layer, a reference database with 10,000 images categorized according to the image modality, orientation, body region examined, and biological system imaged is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. Depending on the complexity of the query, it is processed on the higher layers starting with the scheme layer, where a hierarchical blob representation of image content is provided. Here, local image similarity is assessed by graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements: (i) the central database holds program sources, processing scheme descriptions, images, features, blob trees, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing by addressing daemons running on all connected workstations; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Since manual labeling of reference data is still in progress, the system was used so far for processing primitive queries, i.e. queries regarding the category. However, since all feature transformations in all semantic layers are based the same implemented mechanism, this is sufficient to validate the overall system concept. The leaving-oneout experiments were distributed by the scheduler and controlled via corresponding job lists. The experiments have shown that the IRMA framework offers Transparency regarding the viewpoint of a distributed system and the user, such as (i) location and access Transparency for data and program sources; (ii) replication Transparency for programs in development; (iii) Concurrency Transparency for job processing and feature extraction; (iv) system Transparency at method implementation time; and (v) job distribution Transparency when issuing a query. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

  • A Distributed Architecture for Content-Based Image Retrieval in Medical Applications
    2002
    Co-Authors: M.o. Güld, B. B. Wein, D. Keysers, C. Thies, M. Kohnen, H. Schubert, T.m. Lehmann
    Abstract:

    Image retrieval in medical applications (IRMA) incorporates knowledge from the fields of medicine, image analysis for diagnostic purposes and system engineering. Its implementation as a distributed development platform is fundamental for an efficient interdisciplinary knowledge transfer. The distributed IRMA architecture provides location and access Transparency for its resources, i.e. images, feature vectors and methods, resulting in automatic distribution to all participating work groups, including automated replication functionality. The necessary administration is done via a central database with special attention to automated replication functionality. Concurrency Transparency and automatic distribution of tasks for image processing, feature extraction, feature evaluation and classification allow the utilization of the computational power of all IRMA integrated hosts regardless of their operating system or hardware configuration. Via extensive system Transparency, IRMA drastically simplifies the cooperation of the interdisciplinary development team, allowing all partners to focus on their expert field. In particular, this vastly improves communication and evaluation processes, resulting in much shorter development cycles for new medico-diagnostic methods

T.m. Lehmann - One of the best experts on this subject based on the ideXlab platform.

  • Content-based image retrieval in medical applications for picture archiving and communication systems
    Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, 2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, B. B. Wein
    Abstract:

    Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access Transparency for data, methods, and experiments, (b) replication Transparency for methods in development, (c) Concurrency Transparency for job processing and feature extraction, (d) system Transparency at method implementation time, and (e) job distribution Transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

  • The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications
    2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, Klaus Spitzer, Hermann Ney, B. B. Wein
    Abstract:

    The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). Medical expert knowledge is incorporated into several layers. In the registered data layer, a reference database with 10,000 images categorized according to the image modality, orientation, body region examined, and biological system imaged is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. Depending on the complexity of the query, it is processed on the higher layers starting with the scheme layer, where a hierarchical blob representation of image content is provided. Here, local image similarity is assessed by graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements: (i) the central database holds program sources, processing scheme descriptions, images, features, blob trees, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing by addressing daemons running on all connected workstations; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Since manual labeling of reference data is still in progress, the system was used so far for processing primitive queries, i.e. queries regarding the category. However, since all feature transformations in all semantic layers are based the same implemented mechanism, this is sufficient to validate the overall system concept. The leaving-oneout experiments were distributed by the scheduler and controlled via corresponding job lists. The experiments have shown that the IRMA framework offers Transparency regarding the viewpoint of a distributed system and the user, such as (i) location and access Transparency for data and program sources; (ii) replication Transparency for programs in development; (iii) Concurrency Transparency for job processing and feature extraction; (iv) system Transparency at method implementation time; and (v) job distribution Transparency when issuing a query. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

  • A Distributed Architecture for Content-Based Image Retrieval in Medical Applications
    2002
    Co-Authors: M.o. Güld, B. B. Wein, D. Keysers, C. Thies, M. Kohnen, H. Schubert, T.m. Lehmann
    Abstract:

    Image retrieval in medical applications (IRMA) incorporates knowledge from the fields of medicine, image analysis for diagnostic purposes and system engineering. Its implementation as a distributed development platform is fundamental for an efficient interdisciplinary knowledge transfer. The distributed IRMA architecture provides location and access Transparency for its resources, i.e. images, feature vectors and methods, resulting in automatic distribution to all participating work groups, including automated replication functionality. The necessary administration is done via a central database with special attention to automated replication functionality. Concurrency Transparency and automatic distribution of tasks for image processing, feature extraction, feature evaluation and classification allow the utilization of the computational power of all IRMA integrated hosts regardless of their operating system or hardware configuration. Via extensive system Transparency, IRMA drastically simplifies the cooperation of the interdisciplinary development team, allowing all partners to focus on their expert field. In particular, this vastly improves communication and evaluation processes, resulting in much shorter development cycles for new medico-diagnostic methods

M.o. Güld - One of the best experts on this subject based on the ideXlab platform.

  • Content-based image retrieval in medical applications for picture archiving and communication systems
    Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, 2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, B. B. Wein
    Abstract:

    Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access Transparency for data, methods, and experiments, (b) replication Transparency for methods in development, (c) Concurrency Transparency for job processing and feature extraction, (d) system Transparency at method implementation time, and (e) job distribution Transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

  • The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications
    2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, Klaus Spitzer, Hermann Ney, B. B. Wein
    Abstract:

    The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). Medical expert knowledge is incorporated into several layers. In the registered data layer, a reference database with 10,000 images categorized according to the image modality, orientation, body region examined, and biological system imaged is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. Depending on the complexity of the query, it is processed on the higher layers starting with the scheme layer, where a hierarchical blob representation of image content is provided. Here, local image similarity is assessed by graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements: (i) the central database holds program sources, processing scheme descriptions, images, features, blob trees, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing by addressing daemons running on all connected workstations; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Since manual labeling of reference data is still in progress, the system was used so far for processing primitive queries, i.e. queries regarding the category. However, since all feature transformations in all semantic layers are based the same implemented mechanism, this is sufficient to validate the overall system concept. The leaving-oneout experiments were distributed by the scheduler and controlled via corresponding job lists. The experiments have shown that the IRMA framework offers Transparency regarding the viewpoint of a distributed system and the user, such as (i) location and access Transparency for data and program sources; (ii) replication Transparency for programs in development; (iii) Concurrency Transparency for job processing and feature extraction; (iv) system Transparency at method implementation time; and (v) job distribution Transparency when issuing a query. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

  • A Distributed Architecture for Content-Based Image Retrieval in Medical Applications
    2002
    Co-Authors: M.o. Güld, B. B. Wein, D. Keysers, C. Thies, M. Kohnen, H. Schubert, T.m. Lehmann
    Abstract:

    Image retrieval in medical applications (IRMA) incorporates knowledge from the fields of medicine, image analysis for diagnostic purposes and system engineering. Its implementation as a distributed development platform is fundamental for an efficient interdisciplinary knowledge transfer. The distributed IRMA architecture provides location and access Transparency for its resources, i.e. images, feature vectors and methods, resulting in automatic distribution to all participating work groups, including automated replication functionality. The necessary administration is done via a central database with special attention to automated replication functionality. Concurrency Transparency and automatic distribution of tasks for image processing, feature extraction, feature evaluation and classification allow the utilization of the computational power of all IRMA integrated hosts regardless of their operating system or hardware configuration. Via extensive system Transparency, IRMA drastically simplifies the cooperation of the interdisciplinary development team, allowing all partners to focus on their expert field. In particular, this vastly improves communication and evaluation processes, resulting in much shorter development cycles for new medico-diagnostic methods

C. Thies - One of the best experts on this subject based on the ideXlab platform.

  • Content-based image retrieval in medical applications for picture archiving and communication systems
    Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, 2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, B. B. Wein
    Abstract:

    Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access Transparency for data, methods, and experiments, (b) replication Transparency for methods in development, (c) Concurrency Transparency for job processing and feature extraction, (d) system Transparency at method implementation time, and (e) job distribution Transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

  • The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications
    2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, Klaus Spitzer, Hermann Ney, B. B. Wein
    Abstract:

    The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). Medical expert knowledge is incorporated into several layers. In the registered data layer, a reference database with 10,000 images categorized according to the image modality, orientation, body region examined, and biological system imaged is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. Depending on the complexity of the query, it is processed on the higher layers starting with the scheme layer, where a hierarchical blob representation of image content is provided. Here, local image similarity is assessed by graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements: (i) the central database holds program sources, processing scheme descriptions, images, features, blob trees, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing by addressing daemons running on all connected workstations; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Since manual labeling of reference data is still in progress, the system was used so far for processing primitive queries, i.e. queries regarding the category. However, since all feature transformations in all semantic layers are based the same implemented mechanism, this is sufficient to validate the overall system concept. The leaving-oneout experiments were distributed by the scheduler and controlled via corresponding job lists. The experiments have shown that the IRMA framework offers Transparency regarding the viewpoint of a distributed system and the user, such as (i) location and access Transparency for data and program sources; (ii) replication Transparency for programs in development; (iii) Concurrency Transparency for job processing and feature extraction; (iv) system Transparency at method implementation time; and (v) job distribution Transparency when issuing a query. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

  • A Distributed Architecture for Content-Based Image Retrieval in Medical Applications
    2002
    Co-Authors: M.o. Güld, B. B. Wein, D. Keysers, C. Thies, M. Kohnen, H. Schubert, T.m. Lehmann
    Abstract:

    Image retrieval in medical applications (IRMA) incorporates knowledge from the fields of medicine, image analysis for diagnostic purposes and system engineering. Its implementation as a distributed development platform is fundamental for an efficient interdisciplinary knowledge transfer. The distributed IRMA architecture provides location and access Transparency for its resources, i.e. images, feature vectors and methods, resulting in automatic distribution to all participating work groups, including automated replication functionality. The necessary administration is done via a central database with special attention to automated replication functionality. Concurrency Transparency and automatic distribution of tasks for image processing, feature extraction, feature evaluation and classification allow the utilization of the computational power of all IRMA integrated hosts regardless of their operating system or hardware configuration. Via extensive system Transparency, IRMA drastically simplifies the cooperation of the interdisciplinary development team, allowing all partners to focus on their expert field. In particular, this vastly improves communication and evaluation processes, resulting in much shorter development cycles for new medico-diagnostic methods

D. Keysers - One of the best experts on this subject based on the ideXlab platform.

  • Content-based image retrieval in medical applications for picture archiving and communication systems
    Medical Imaging 2003: PACS and Integrated Medical Information Systems: Design and Evaluation, 2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, B. B. Wein
    Abstract:

    Picture archiving and communication systems (PACS) aim to efficiently provide the radiologists with all images in a suitable quality for diagnosis. Modern standards for digital imaging and communication in medicine (DICOM) comprise alphanumerical descriptions of study, patient, and technical parameters. Currently, this is the only information used to select relevant images within PACS. Since textual descriptions insufficiently describe the great variety of details in medical images, content-based image retrieval (CBIR) is expected to have a strong impact when integrated into PACS. However, existing CBIR approaches usually are limited to a distinct modality, organ, or diagnostic study. In this state-of-the-art report, we present first results implementing a general approach to content-based image retrieval in medical applications (IRMA) and discuss its integration into PACS environments. Usually, a PACS consists of a DICOM image server and several DICOM-compliant workstations, which are used by radiologists for reading the images and reporting the findings. Basic IRMA components are the relational database, the scheduler, and the web server, which all may be installed on the DICOM image server, and the IRMA daemons running on distributed machines, e.g., the radiologists’ workstations. These workstations can also host the web-based front-ends of IRMA applications. Integrating CBIR and PACS, a special focus is put on (a) location and access Transparency for data, methods, and experiments, (b) replication Transparency for methods in development, (c) Concurrency Transparency for job processing and feature extraction, (d) system Transparency at method implementation time, and (e) job distribution Transparency when issuing a query. Transparent integration will have a certain impact on diagnostic quality supporting both evidence-based medicine and case-based reasoning.

  • The IRMA Project: A State of the Art Report on Content-Based Image Retrieval in Medical Applications
    2003
    Co-Authors: T.m. Lehmann, M.o. Güld, D. Keysers, C. Thies, M. Kohnen, H. Schubert, Benedikt Fischer, Klaus Spitzer, Hermann Ney, B. B. Wein
    Abstract:

    The objective of this work is to develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. Stepwise content analysis of medical images results in six layers of information modeling (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). Medical expert knowledge is incorporated into several layers. In the registered data layer, a reference database with 10,000 images categorized according to the image modality, orientation, body region examined, and biological system imaged is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. Depending on the complexity of the query, it is processed on the higher layers starting with the scheme layer, where a hierarchical blob representation of image content is provided. Here, local image similarity is assessed by graph matching. The multilayer processing is implemented using a distributed system designed with only three core elements: (i) the central database holds program sources, processing scheme descriptions, images, features, blob trees, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing by addressing daemons running on all connected workstations; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. Since manual labeling of reference data is still in progress, the system was used so far for processing primitive queries, i.e. queries regarding the category. However, since all feature transformations in all semantic layers are based the same implemented mechanism, this is sufficient to validate the overall system concept. The leaving-oneout experiments were distributed by the scheduler and controlled via corresponding job lists. The experiments have shown that the IRMA framework offers Transparency regarding the viewpoint of a distributed system and the user, such as (i) location and access Transparency for data and program sources; (ii) replication Transparency for programs in development; (iii) Concurrency Transparency for job processing and feature extraction; (iv) system Transparency at method implementation time; and (v) job distribution Transparency when issuing a query. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.

  • A Distributed Architecture for Content-Based Image Retrieval in Medical Applications
    2002
    Co-Authors: M.o. Güld, B. B. Wein, D. Keysers, C. Thies, M. Kohnen, H. Schubert, T.m. Lehmann
    Abstract:

    Image retrieval in medical applications (IRMA) incorporates knowledge from the fields of medicine, image analysis for diagnostic purposes and system engineering. Its implementation as a distributed development platform is fundamental for an efficient interdisciplinary knowledge transfer. The distributed IRMA architecture provides location and access Transparency for its resources, i.e. images, feature vectors and methods, resulting in automatic distribution to all participating work groups, including automated replication functionality. The necessary administration is done via a central database with special attention to automated replication functionality. Concurrency Transparency and automatic distribution of tasks for image processing, feature extraction, feature evaluation and classification allow the utilization of the computational power of all IRMA integrated hosts regardless of their operating system or hardware configuration. Via extensive system Transparency, IRMA drastically simplifies the cooperation of the interdisciplinary development team, allowing all partners to focus on their expert field. In particular, this vastly improves communication and evaluation processes, resulting in much shorter development cycles for new medico-diagnostic methods