Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIP.2010.5652386
Title: Automatic cell classification and population estimation in blastocystis autophagy images
Authors: Xiong, W.
Lim, J.H.
Ong, S.H. 
Liu, J.
Jing, Y.
Tan, K.S.W.
Keywords: Automatic
Autophagy
Blastocystis
Classification
Population estimation
Issue Date: 2010
Citation: Xiong, W., Lim, J.H., Ong, S.H., Liu, J., Jing, Y., Tan, K.S.W. (2010). Automatic cell classification and population estimation in blastocystis autophagy images. Proceedings - International Conference on Image Processing, ICIP : 4349-4352. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIP.2010.5652386
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. © 2010 IEEE.
Source Title: Proceedings - International Conference on Image Processing, ICIP
URI: http://scholarbank.nus.edu.sg/handle/10635/69468
ISBN: 9781424479948
ISSN: 15224880
DOI: 10.1109/ICIP.2010.5652386
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