课题基金 / 基金详情

A MULTI-USER DIGITAL IMAGE ACQUISTITION ANALYSIS AND DIS

A MULTI-USER DIGITAL IMAGE ACQUISTITION ANALYSIS AND DIS
多用户数字图像采集分析与显示
批准号:
3519138
负责人:
DEAN E HILLMAN
金额:
$20.85万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
1985
资助国家:
美国
项目状态:
已结题
起止时间:
1985-01-01 至 1985-12-31

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中文摘要
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英文摘要
This proposal requests funds for upgrading a multi-user image analysis facility to be shared by various investigators of the Departments of Physiology, Cell Biology and Surgery in the New York University Medical Center. The central computational facility is operational and essential devices are being requested to upgrade the system to "image analysis status" with "full graphic capability" as a shared resource. The objective is to have available a critical mass of software and hardware embodied in a single system which can carry out the broad range of image recording, analysis, retrieval, display and creation allowing investigators to collect data, analyze and model experimental system all within a common data base. The pieces of essential equipment are related to digitizing (camera, digitizer, microscopic stage and controller), display (workstations and hard copy unit), rapid retrieval (video disk) and graphics generation (vector display module). These will be configured with an existing basic Gould-DeAnza image analysis and display system that is interfaced to a VAX 780 control processor. The completed system will be a state-of-the-art image processing system for use in a number of research programs in cell biology and neurobiology as well as reconstruction of images for dynamic analysis as models of working systems or for defining surfaces of complex images. More specifically, the system will be used to: 1) record and analyze ultrastructure of various tissue types, with an emphasis on membranes and subcellular organelles, 2) record and quantitatively analyze nervous system component parts and their relationships (neuronal arbors, cell groups and pathways), 3) record multiple electrical events for spatial and temporal analysis, 4) display analyze multiple, complex objects and 5) recreate images from data in models that can depict structural-functional relationships and dynamic processes.
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