• 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