Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images-Reference-Cited by-同舟云学术

Comparison of Four Automatic Classifiers for Cancer Cell Phenotypes Using M-Phase Features Extracted from Brightfield Microscopy Images

Published:2020 Issue: Volume: Page:406-419
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Siles Francisco,Mora-Zúñga Andrés,Quiros Steve

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-41005-6_28

Reference37 articles.

1. Abbas, S.S., Dijkstra, T., Heskes, T.: A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinform. 15(1), 342 (2014). https://doi.org/10.1186/1471-2105-15-342

2. Buggenthin, F., et al.: An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy. BMC Bioinform. 14, 297 (2013). https://doi.org/10.1186/1471-2105-14-297

3. Calderón, S., Castro, J., Sáenz, A., Siles, F., Mora, R.: Automatic cell segmentation and tracking in the NF-kB pathway using time-lapse fluorescence microscopy videos without nuclear staining. In: VI Latin American Conference on Biomedical Engineering CLAIB 2014, Paraná, Argentina (2014)

4. Calderón, S., Siles, F.: Deceived bilateral filter for improving the classification of football players from TV broadcast. In: 3rd IEEE International Work-Conference on Bioinspired Intelligence, pp. 98–105 (2014). https://doi.org/10.1109/IWOBI.2014.6913946

5. Calderón, S., Sáenz, A., Mora, R., Siles, F., Orozco, I., Buemi, M.E.: A novel image abstraction approach to improve the performance of a cell tracking system. In: 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI), pp. 81–88 (2015). https://doi.org/10.1109/IWOBI.2015.7160148