Clustering-driven watershed adaptive segmentation of bubble image View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2010-10

AUTHORS

Kai-jun Zhou, Chun-hua Yang, Wei-hua Gui, Can-hui Xu

ABSTRACT

In order to extract froth morphological feature, a bubble image adaptive segmentation method was proposed. Considering the image’s low contrast and weak froth edges, froth image was coarsely segmented by using fuzzy c means (FCM) algorithm. Through the attributes of size and shape pattern spectrum, the optimal morphological structuring element was determined. According to the optimal parameters, some image noises were removed with an improved area opening and closing by reconstruction operation, which consist of image regional markers, and the bubbles were finely separated from each other by watershed transform. The experimental results show that the structural element can be determined adaptively by shape and size pattern spectrum, and the froth image is segmented accurately. Compared with other froth image segmentation method, the proposed method achieves much high accuracy, based on which, the bubble size and shape features are extracted effectively. More... »

PAGES

1049-1057

References to SciGraph publications

  • 2008-12. New two-dimensional fuzzy C-means clustering algorithm for image segmentation in JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY
  • 2001. Shape Preserving Filament Enhancement Filtering in MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2001
  • 2006-01. A Lattice Approach to Image Segmentation in JOURNAL OF MATHEMATICAL IMAGING AND VISION
  • Identifiers

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    http://scigraph.springernature.com/pub.10.1007/s11771-010-0597-y

    DOI

    http://dx.doi.org/10.1007/s11771-010-0597-y

    DIMENSIONS

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