Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering - PubMed (original) (raw)
. 2013 Dec;32(12):2274-86.
doi: 10.1109/TMI.2013.2280380. Epub 2013 Aug 30.
- PMID: 24001988
- DOI: 10.1109/TMI.2013.2280380
Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering
Firas Mualla et al. IEEE Trans Med Imaging. 2013 Dec.
Abstract
We present a novel machine learning-based system for unstained cell detection in bright-field microscope images. The system is fully automatic since it requires no manual parameter tuning. It is also highly invariant with respect to illumination conditions and to the size and orientation of cells. Images from two adherent cell lines and one suspension cell line were used in the evaluation for a total number of more than 3500 cells. Besides real images, simulated images were also used in the evaluation. The detection error was between approximately zero and 15.5% which is a significantly superior performance compared to baseline approaches.
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