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Automated segmentation in confocal images using a density clustering method

[+] Author Affiliations
Po-Kwok Chan

City University of Hong Kong, Department of Biology and Chemistry, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

Shuk-Han Cheng

City University of Hong Kong, Department of Biology and Chemistry, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

Ting-Chung Poon

City University of Hong Kong, Department of Electronic Engineering, 83 Tat Chee Avenue, Kowloon, Hong Kong, China and Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061

J. Electron. Imaging. 16(4), 043003 (November 29, 2007). doi:10.1117/1.2804279
History: Received July 24, 2006; Revised April 10, 2007; Accepted May 11, 2007; Published November 29, 2007
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Confocal microscopy provides a powerful tool for biologists to investigate gene expression in a 3D manner. However, due to the inherent properties of confocal images, it is difficult to accurately segregate foreground signals from the background using direct thresholding. Therefore, there is a need for a segmentation algorithm that can be used with fluorescent confocal images of gene expression. We present an automatic segmentation algorithm for thresholding confocal images of gene expression in biological samples. The algorithm, called density-based segmentation (DBS), is modified from a noise-tolerant data clustering algorithm (DENCLUE). We demonstrate the utility of this algorithm in different synthetic images as well as in confocal images of zebrafish embryos, with comparison to Otsu’s algorithm, which employs direct thresholding. The results of segmentation in synthetic images show that the DBS algorithm is noise-tolerant and is able to distinguish two objects located close to each other. In addition, the results of segmentation in confocal images show that the DBS algorithm can threshold objects while preserving morphological details of internal structures. Therefore, the proposed DBS algorithm is a better segmentation technique than direct thresholding in the segmentation of fluorescent confocal images.

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© 2007 SPIE and IS&T

Citation

Po-Kwok Chan ; Shuk-Han Cheng and Ting-Chung Poon
"Automated segmentation in confocal images using a density clustering method", J. Electron. Imaging. 16(4), 043003 (November 29, 2007). ; http://dx.doi.org/10.1117/1.2804279


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