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3. Design a recursive algorithm to extract speci c contents of interest from CAD structured reports using a knowledge-based approach combined with the DICOM standard. 4. Implement Web-based client interfaces to allow remote data mining from the structured reports and link images archived in the Data Grid by quantitative retrieval method, rather than patient identi ers or study UIDs. The work ow of these steps is illustrated in Figure 26.30. 26.8.6.3 Computational Services in Data Grid Computational Services Architecture in the Data Grid Data Grid uses the Globus Toolkit 4.02. Although in Data Grid for storage we use mostly data transfer and storage services, other computational services in the Globus Toolkit are readily available. In the Grid environment an application component in computing can be implemented in different source les, each complied to run in a different type of target architecture. Exact replicas of the executable le can be stored in many locations, which helps reduce execution time. Data les can also be replicated in various locations. Each le has a description of its contents in terms of application-speci c metadata. The Metadata Service, including Catalog Service (see Figure 21.5, userlevel middleware), responds to queries based on application-speci c metadata and returns the logical names of les containing the required data, if they already exist. Given a logical le name that uniquely identi es a le without specifying a location, the Replica Location Service (RLS) (Fig. 21.5, core middleware) can be used to nd physical location for the le on the Grid. Figure 26.31A shows the operation architecture of the computational services in the Grid environment. First, the client requests resources from MDS (monitoring and discovery system) server in the core middleware (Fig. 26.31A, step 1), which manages the resources and distributes the jobs to the computational services. The index

n autopilot can reduce the workload on the helmsman and navigator. For longer short-handed passages, it can allow proper time for navigation and pilotage. 13.1. Types of Autopilot There are two basic types of autopilots:

Table 8.3 TYPE DATE TIMESTAMP(p) Date Types DESCRIPTION Describes a date and time between January 1, 4712 B.C. and December 31, 9999 A.D. A date with the ability to store fractional seconds as specified by the argument p, which must be between 0 and 9. The default is 6. Same as timestamp, but the timezone offset is also stored. Same as timestamp but normalized to the database s timezone upon storage and translated to the session timezone upon retrieval. Stores a period of time in years and month. The argument year_p is the number of digits in the year field, which must be between 0 and 9 and defaults to 2.

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The Filter Stylize submenu offers access to a triad of filters that enhance the edges in an image. The most popular of these is undoubtedly Emboss, which adds dimension to an image by making it look as if it were carved in relief. The other two, Find Edges and Trace Contour, are less commonly applied but every bit as capable and deserving of your attention.

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the narrow dynamic range of 103 to 104 to a large light relative exposure of 1 to 50 (Fig. 3.13B). If hardcopy is needed, a large latitude lm can be used that covers the dynamic range of the light exposure from 1 to 50, as shown in quadrant C, these output signals will register the entire optical density (OD) range from OD 0.2 to OD 2.8 on the lm. The total system response including the imaging plate, the look-up table, and the lm subject to this exposure range is depicted as curve I in quadrant D. The system-response curve, relating the relative exposure on the plate and the OD of the output lm, shows a high gamma value and is quite linear. This example demonstrates how the system accommodates a high exposure level with a narrow exposure range. When lm is not needed, curve I in quadrant D can be converted to digital numbers with a range of 10 to 12 bits. Consider example II, in which the plate receives a lower exposure level but with wider exposure range. The CR system automatically selects a different look-up table in the image processor to accommodate this range of exposure so that the output signals again span the entire light exposure range form 1 to 50. The system-response curve is shown as curve II in quadrant D. The key in selecting the correct look-up table is that the range of the exposure has to span the total light exposure from 1 to 50. Note that in both examples the entire useful optical density range for diagnostic radiology is utilized.

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