• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Slow Feature Analysis for Mitotic Event Recognition

Vol. 11, No. 3, March 30, 2017
10.3837/tiis.2017.03.023, Download Paper (Free):

Abstract

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
J. Chu, H. Liang, Z. Tong and W. Lu, "Slow Feature Analysis for Mitotic Event Recognition," KSII Transactions on Internet and Information Systems, vol. 11, no. 3, pp. 1670-1683, 2017. DOI: 10.3837/tiis.2017.03.023.

[ACM Style]
Jinghui Chu, Hailan Liang, Zheng Tong, and Wei Lu. 2017. Slow Feature Analysis for Mitotic Event Recognition. KSII Transactions on Internet and Information Systems, 11, 3, (2017), 1670-1683. DOI: 10.3837/tiis.2017.03.023.

[BibTeX Style]
@article{tiis:21406, title="Slow Feature Analysis for Mitotic Event Recognition", author="Jinghui Chu and Hailan Liang and Zheng Tong and Wei Lu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2017.03.023}, volume={11}, number={3}, year="2017", month={March}, pages={1670-1683}}