3d microscopy images contain abundant astronomical data, rendering 3d microscopy imageprocessing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of ...
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ISBN:
(纸本)9780819493583
3d microscopy images contain abundant astronomical data, rendering 3d microscopy imageprocessing time-consuming and laborious on a central processing unit (CPU). To solve these problems, many people crop a region of interest (ROI) of the input image to a small size. Although this reduces cost and time, there are drawbacks at the imageprocessing level, e. g., the selected ROI strongly depends on the user and there is a loss in original image information. To mitigate these problems, we developed a 3d microscopy imageprocessing tool on a graphics processing unit (GPU). Our tool provides efficient and various automatic thresholding methods to achieve intensity-based segmentation of 3d microscopy images. Users can select the algorithm to be applied. Further, the imageprocessing tool provides visualization of segmented volume data and can set the scale, transportation, etc. using a keyboard and mouse. However, the 3d objects visualized fast still need to be analyzed to obtain information for biologists. To analyze 3dmicroscopicimages, we need quantitative data of the images. Therefore, we label the segmented3d objects within all 3dmicroscopicimages and obtain quantitative information on each labeled object. This information can use the classification feature. A user can select the object to be analyzed. Our tool allows the selected object to be displayed on a new window, and hence, more details of the object can be observed. Finally, we validate the effectiveness of our tool by comparing the CPU and GPU processing times by matching the specification and configuration.
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