DocumentCode :
243724
Title :
Segmentation and Splitting of Touching Vaginal Bacteria Based on Superpixel and Effective Distance
Author :
Youyi Song ; Dong Ni ; Liang He ; Siping Chen ; Baiying Lei ; Tianfu Wang
Author_Institution :
Dept. of Biomed. Eng., Shenzhen Univ., Shenzhen, China
fYear :
2014
fDate :
14-14 Dec. 2014
Firstpage :
976
Lastpage :
981
Abstract :
In this paper, a new method for segmentation and splitting of touching vaginal bacteria based on super pixel method is proposed. Feature fusion is integrated with kernel-based support vector machine (SVM) for bacteria segmentation. After segmentation by super pixel, the touching bacteria regions are further separated according to the defined effective distance. Finally, the separated bacteria are counted finally for the performance evaluation. Our experimental results show that the proposed method has achieved promising segmentation result. Moreover, compared to the state-of-the-arts method, better segmentation results have also been achieved.
Keywords :
image fusion; image segmentation; medical image processing; microorganisms; support vector machines; feature fusion; kernel-based SVM; kernel-based support vector machine; performance evaluation; superpixel method; touching vaginal bacteria segmentation; touching vaginal bacteria splitting; Feature extraction; Image color analysis; Image segmentation; Kernel; Microorganisms; Shape; Support vector machines; Segmentation; effective distance; splitting; superpixel; vaginal bacteria;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshop (ICDMW), 2014 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4799-4275-6
Type :
conf
DOI :
10.1109/ICDMW.2014.172
Filename :
7022702
Link To Document :
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