Abstract
The automatic analysis of cancer cells has gained increasing relevance given the amount of data that biology researchers have to analyze. However, most biology researchers still analyze cells by visual inspection alone, which is time consuming and prone to induce subjective bias. This makes automatic cell image analysis essential for large scale, objective studies of cells.
While the classic approach for automatic cell detection is to use image segmentation, in the case of in vivo brightfield images, such approach is not robust to image quality changes. To detect cells with robustness and increased performance we propose the use of local interest point detectors. We perform a comparison study between the use of the Laplacian of Gaussian filter, a Bank of Ring Filters and local convergence filters.
Based on experimental results we found that the Laplacian of Gaussian filter outperformed all other in cell detection obtaining an accuracy of 78%. Additionally, through the analysis of shape fit, we found that the Laplacian of Gaussian filter obtained a better approximation to the shape of the cells having a Dice’s coefficient of 81%.
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References
Quelhas, P., Marcuzzo, M., Mendonça, A.M., Oliveira, M.J., Campilho, A.: Cancer cell detection and invasion depth estimation in brightfield images. In: BMVC, pp. 1–10 (2009)
Usaj, M., Torkar, D., Kanduser, M., Miklavcic, D.: Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast image. Journal of Microscopy 241(3), 303–314 (2010)
Meijering, E.: Cell segmentation: 50 years down the road (life sciences). IEEE Signal Processing Magazine 29(5), 140–145 (2012)
Esteves, T., Quelhas, P., Mendonça, A.M., Campilho, A.: Gradient convergence filters for cell nuclei detection: a comparison study with a phase based approach. MVAP 23(4), 623–638 (2012)
Eom, S., Bise, R., Kanade, T.: Detection of hematopoietic stem cells in microscopy images using a bank of ring filters. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 137–140 (April 2010)
Lindeberg, T.: Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics 21(2), 224–270 (1994)
Kobatake, H., Hashimoto, S.: Convergence index filter for vector fields. IEEE Trans. on Image Processing 8(8), 1029–1038 (1999)
Wei, J., Hagihara, Y., Kobatake, H.: Detection of cancerous tumors on chest x-ray images candidate detection filter and its evaluation. In: Proc. of International Conference on Image Analysis and Processing (ICIP), pp. 397–401 (1999)
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Esteves, T., Oliveira, M.J., Quelhas, P. (2013). Cancer Cell Detection and Morphology Analysis Based on Local Interest Point Detectors. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_74
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DOI: https://doi.org/10.1007/978-3-642-38628-2_74
Publisher Name: Springer, Berlin, Heidelberg
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