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MindSeer: a portable and extensible tool for visualization of structural and functional neuroimaging data.

Moore EB, Poliakov AV, Lincoln P, Brinkley JF - BMC Bioinformatics (2007)

Bottom Line: A number of applications are available that include some aspect of 3-D visualization, including both free and commercial products.We describe the design and implementation of the system, as well as several case studies that demonstrate its utility.Its architecture and unique features also allow it to be extended into other visualization domains within biomedicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Structural Informatics Group, Department of Biological Structure, University of Washington, Seattle, USA. eider@u.washington.edu

ABSTRACT

Background: Three-dimensional (3-D) visualization of multimodality neuroimaging data provides a powerful technique for viewing the relationship between structure and function. A number of applications are available that include some aspect of 3-D visualization, including both free and commercial products. These applications range from highly specific programs for a single modality, to general purpose toolkits that include many image processing functions in addition to visualization. However, few if any of these combine both stand-alone and remote multi-modality visualization in an open source, portable and extensible tool that is easy to install and use, yet can be included as a component of a larger information system.

Results: We have developed a new open source multimodality 3-D visualization application, called MindSeer, that has these features: integrated and interactive 3-D volume and surface visualization, Java and Java3D for true cross-platform portability, one-click installation and startup, integrated data management to help organize large studies, extensibility through plugins, transparent remote visualization, and the ability to be integrated into larger information management systems. We describe the design and implementation of the system, as well as several case studies that demonstrate its utility. These case studies are available as tutorials or demos on the associated website: http://sig.biostr.washington.edu/projects/MindSeer.

Conclusion: MindSeer provides a powerful visualization tool for multimodality neuroimaging data. Its architecture and unique features also allow it to be extended into other visualization domains within biomedicine.

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Related in: MedlinePlus

MindSeer Library. A screen capture showing the tree form of the MindSeer Library, where the Library is specified by an XML file defining the input/output file specifications for a given visualization. This particular library is organized by coordinate space (Magnet), subject (P163), type (Functional, Structural), file type (Image, Surface, Volume) and modality (fMRI, EEG). All of these metadata are internally stored in free form XML tags.
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Figure 2: MindSeer Library. A screen capture showing the tree form of the MindSeer Library, where the Library is specified by an XML file defining the input/output file specifications for a given visualization. This particular library is organized by coordinate space (Magnet), subject (P163), type (Functional, Structural), file type (Image, Surface, Volume) and modality (fMRI, EEG). All of these metadata are internally stored in free form XML tags.

Mentions: An important feature of MindSeer is that it integrates data management with visualization. By combining these functions, MindSeer provides a quick and easy way to create and experiment with scenes, and to automatically find data without user intervention. The application manages data in an XML file, called the Library, which contains file locations and associated metadata. Data can be tagged with any metadata, but the interface makes it easy to attach certain core tags, including the coordinate space (magnet, Talairach, etc.), the subject or patient identifier, whether the image is structural or functional, and the modality (MRI, fMRI, etc.). These core tags are used to automatically generate a consistent tree for easily browsing the data (Figure 2), which is sorted by coordinate space, subject, function versus structure, and modality. The system also infers the tags of a new data file based on where it is inserted into the tree. The core tags and exact tree structure are specified in an XML based template file that can be edited for other domains. Simple scripts can be created to generate the XML library file automatically from a database.


MindSeer: a portable and extensible tool for visualization of structural and functional neuroimaging data.

Moore EB, Poliakov AV, Lincoln P, Brinkley JF - BMC Bioinformatics (2007)

MindSeer Library. A screen capture showing the tree form of the MindSeer Library, where the Library is specified by an XML file defining the input/output file specifications for a given visualization. This particular library is organized by coordinate space (Magnet), subject (P163), type (Functional, Structural), file type (Image, Surface, Volume) and modality (fMRI, EEG). All of these metadata are internally stored in free form XML tags.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2099449&req=5

Figure 2: MindSeer Library. A screen capture showing the tree form of the MindSeer Library, where the Library is specified by an XML file defining the input/output file specifications for a given visualization. This particular library is organized by coordinate space (Magnet), subject (P163), type (Functional, Structural), file type (Image, Surface, Volume) and modality (fMRI, EEG). All of these metadata are internally stored in free form XML tags.
Mentions: An important feature of MindSeer is that it integrates data management with visualization. By combining these functions, MindSeer provides a quick and easy way to create and experiment with scenes, and to automatically find data without user intervention. The application manages data in an XML file, called the Library, which contains file locations and associated metadata. Data can be tagged with any metadata, but the interface makes it easy to attach certain core tags, including the coordinate space (magnet, Talairach, etc.), the subject or patient identifier, whether the image is structural or functional, and the modality (MRI, fMRI, etc.). These core tags are used to automatically generate a consistent tree for easily browsing the data (Figure 2), which is sorted by coordinate space, subject, function versus structure, and modality. The system also infers the tags of a new data file based on where it is inserted into the tree. The core tags and exact tree structure are specified in an XML based template file that can be edited for other domains. Simple scripts can be created to generate the XML library file automatically from a database.

Bottom Line: A number of applications are available that include some aspect of 3-D visualization, including both free and commercial products.We describe the design and implementation of the system, as well as several case studies that demonstrate its utility.Its architecture and unique features also allow it to be extended into other visualization domains within biomedicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Structural Informatics Group, Department of Biological Structure, University of Washington, Seattle, USA. eider@u.washington.edu

ABSTRACT

Background: Three-dimensional (3-D) visualization of multimodality neuroimaging data provides a powerful technique for viewing the relationship between structure and function. A number of applications are available that include some aspect of 3-D visualization, including both free and commercial products. These applications range from highly specific programs for a single modality, to general purpose toolkits that include many image processing functions in addition to visualization. However, few if any of these combine both stand-alone and remote multi-modality visualization in an open source, portable and extensible tool that is easy to install and use, yet can be included as a component of a larger information system.

Results: We have developed a new open source multimodality 3-D visualization application, called MindSeer, that has these features: integrated and interactive 3-D volume and surface visualization, Java and Java3D for true cross-platform portability, one-click installation and startup, integrated data management to help organize large studies, extensibility through plugins, transparent remote visualization, and the ability to be integrated into larger information management systems. We describe the design and implementation of the system, as well as several case studies that demonstrate its utility. These case studies are available as tutorials or demos on the associated website: http://sig.biostr.washington.edu/projects/MindSeer.

Conclusion: MindSeer provides a powerful visualization tool for multimodality neuroimaging data. Its architecture and unique features also allow it to be extended into other visualization domains within biomedicine.

Show MeSH
Related in: MedlinePlus