Mualla F, Schöll S, Sommerfeldt B, Maier A, Steidl S, Buchholz R, Hornegger J (2014)
Publication Type: Conference contribution, Original article
Publication year: 2014
Edited Volumes: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages Range: 377-384
Conference Proceedings Title: Lecture Notes in Computer Science, Volume 8675, MICCAI 2014 Proceedings, Part III
Event location: Boston, MA, USA
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2014/Mualla14-UUC.pdf
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
APA:
Mualla, F., Schöll, S., Sommerfeldt, B., Maier, A., Steidl, S., Buchholz, R., & Hornegger, J. (2014). Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm. In Lecture Notes in Computer Science, Volume 8675, MICCAI 2014 Proceedings, Part III (pp. 377-384). Boston, MA, USA.
MLA:
Mualla, Firas, et al. "Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm." Proceedings of the Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Boston, MA, USA 2014. 377-384.
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