Third, novel digital atlasing features are developed, such as supporting multiple label sets, dynamic selection and grouping of labels, and synchronized display of ontological data. The researcher can also refine the search criteria putamen by adding more terms, such as a gene name lcn2 or the name of the disease Parkinson's. Also, the delineations for each individual atlas can be dynamically shown or hidden to help the researcher pinpoint the location of the unusual formation. Proceedings of Healthgrid Once the region of interest has been identified, the researcher would then like to search for other image data that show expression patterns for gene lcn2, were collected to study Parkinson's disease, and contain the region of interest. JPF was chosen since it is extremely lightweight, has a low learning curve, and is extremely flexible and extensible. For example, to reference data against a digital atlas, the system will dynamically display the associated anatomical labels for the region of interest, highlight the current relationships in an ontology graph display, and list the annotations for the current data layer. To address this issue, the design of the Registration Workspace is built around registration algorithm plug-ins that can be selected by the user. Factors that influence what type of registration algorithms to use include image modality of the data, dimensionality of the source and template data 2D-2D, 3D-3D, or 2D-3Dspeed or time requirements, and accuracy, such as whole brain or structure accuracy [ 4 ].
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Search Workspace The primary goal of the Search Workspace is to provide a federated search where a single query is used to search across multiple data sources, making the underlying heterogeneous data services transparent to the user.
Second, spatially aligning or registering image data to an atlas or to each other is a complex problem with many solutions.
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MBAT empowers researchers to discover correlations among disconnected and disparate data by providing a unified environment for bringing together distributed reference resources, a user's image data, and biological atlases into the same spatial or semantic context.
Future work includes extending the Comparison Viewer Workspace to include data that has been semantically registered to an atlas, extending the graph viewer plug-ins to support more ontological relationship types, extending the search engine to allow selection of query terms that are specific to a particular data source, and extending the Registration Workspace to allow real-time comparison of the results of different algorithms.
What sets our work apart from the others are three key features. Rapid automated algorithm for aligning and reslicing PET images.
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First, each data source typically has a different search interface and result format which makes reconciling data difficult.
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For example, selecting a parent node, such as the forebrain, will automatically select all the sub-structures in the ontology graph, such as the amygdala, basal ganglia, and cerebral cortex, and highlight them in the Comparison Viewer. Use Case Consider a researcher who has collected a series of slices for an experiment for Parkinson's disease in relation to the Lipocalin-2 gene lcn2 using a mouse model.
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Journal of Computer Assisted Tomography. Future versions of MBAT will benefit from switching plug-in frameworks as it will make it immediately interoperable with OSGi applications, such as the chemo- and bioinformatics plug-ins found in Bioclipse [ 2627 ].
The results page groups the results by data source and data type with all the annotations in tabular form.