Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation

Sheng Huang, Mingchen Gao, Dan Yang, Xiaolei Huang, Ahmed Elgammal, Xiaohong Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We propose a novel medical image segmentation algorithm by transductively inferring the labels. In this approach, superpixels are first generated to incorporate the local spatial information and also to speed up the segmentation. The segmentation task can be deemed as an unbalanced superpixels labeling problem due to the fact that the region of interest is only a small fraction compared to the whole image. We present a new transductive learning-based algorithm called Class Averaging Graph-based Transduction (CAGT) to avoid the biased labeling caused by the imbalance. The proposed algorithm was applied to the automatic cervigram image segmentation to demonstrate it effectiveness.

Original languageEnglish (US)
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1556-1559
Number of pages4
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July

Other

Other12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
CountryUnited States
CityBrooklyn
Period4/16/154/19/15

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging
  • Biomedical Engineering

Keywords

  • Graph Learning
  • Image Segmentation
  • Semi-supervised Learning
  • Transductive Learning
  • Unbalanced Classification

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  • Cite this

    Huang, S., Gao, M., Yang, D., Huang, X., Elgammal, A., & Zhang, X. (2015). Unbalanced graph-based transduction on superpixels for automatic cervigram image segmentation. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 1556-1559). [7164175] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164175