DocumentCode :
3684797
Title :
Bacterial foraging based edge detection for cell image segmentation
Author :
Yongsheng Pan;Tao Zhou;Yong Xi
Author_Institution :
Shaanxi Key Lab of Speech &
fYear :
2015
Firstpage :
3873
Lastpage :
3876
Abstract :
Edge detection is the most popular and common choices for cell image segmentation, in which local searching strategies are commonly used. In spite of their computational efficiency, traditional edge detectors, however, may either produce discontinued edges or rely heavily on initializations. In this paper, we propose a bacterial foraging based edge detection (BFED) algorithm for cell image segmentation. We model the gradients of intensities as the nutrient concentration and propel bacteria to forage along nutrient-rich locations via mimicking the behavior of Escherichia coli, including the chemotaxis, swarming, reproduction, elimination and dispersal. As a nature-inspired evolutionary technique, this algorithm can identify the desired edges and mark them as the tracks of bacteria. We have evaluated the proposed algorithm against the Canny, SUSAN, Verma´s and an active contour model (ACM) based edge detectors on both synthetic and real cell images. Our results suggest that the BFED algorithm can identify boundaries more effectively and provide more accurate cell image segmentation.
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE
ISSN :
1094-687X
Electronic_ISBN :
1558-4615
Type :
conf
DOI :
10.1109/EMBC.2015.7319239
Filename :
7319239
Link To Document :
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