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Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU

Title
Intensity-based segmentation and visualization of cells in 3D microscopic images using the GPU
Authors
Kang M.-S.Lee J.-E.Jeon W.-K.Choi H.-K.Kim M.-H.
Ewha Authors
김명희
SCOPUS Author ID
김명희scopus
Issue Date
2013
Journal Title
Progress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN
1605-7422JCR Link
Citation
vol. 8589
Indexed
SCOPUS scopus
Abstract
3D microscopy images contain abundant astronomical data, rendering 3D microscopy image processing 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 image processing 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 image processing 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 image processing 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 3D microscopic images, we need quantitative data of the images. Therefore, we label the segmented 3D objects within all 3D microscopic images 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. © 2013 Copyright SPIE.
DOI
10.1117/12.2004934
ISBN
9780819493583
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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