Automatic cell classification and population estimation in blastocystis autophagy images

Wei Xiong, Joo Hwee Lim, S. H. Ong, Jiang Liu, Yin Jing, Kevin S.W. Tan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Blastocystis is a unicellular but polymorphic protozoan parasite causing digestive diseases in humans. Autophagy, a self-degradation process, is only recently found in Blastocystis. Identifying and enumerating autophagic Blastocystis cells using fluorescent microscopy are important in biology. Doing this manually is laborious and error-prone. This paper proposes image analysis techniques to automate the process. The difficulties are poor image quality and large variations in illumination and cell morphology. We divide the cells into several sub-classes of different morphology. Support vector machines are used to learn domain knowledge and classify the cells. Validation experiments on separate data sets show reliable performance for manually segmented cells with sensitivity 82.2% and specificity 86.7%. For automatically segmented cells, the sensitivity is the same. However, the specificity drops down to 68.4%. To our knowledge, this is the first attempt in automatic processing these images.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages4349-4352
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: 26 Sep 201029 Sep 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong
Period26/09/1029/09/10

Keywords

  • Automatic
  • Autophagy
  • Blastocystis
  • Classification
  • Population estimation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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