Abstract
We have developed a new semi-automated method for segmenting images of biological cells seeded at low density on tissue culture substrates, which we use to improve the generation of reference data for the evaluation of automated segmentation algorithms. The method was designed to mimic manual cell segmentation and is based on a model of human visual perception. We demonstrate a need for automated methods to assist with the generation of reference data by comparing several sets of masks from manually segmented cell images created by multiple independent hand-selections of pixels that belong to cell edges. We quantify the differences in these manually segmented masks and then compare them with masks generated from our new segmentation method which we use on cell images acquired to ensure very sharp, clear edges. The resulting masks from 16 images contain 71 cells and show that our semi-automated method for reference data generation locates cell edges more consistently than manual segmentation alone and produces better edge detection than other techniques like 5-means clustering and active contour segmentation for our images.
This contribution of NIST, an agency of the U.S. government, is not subject to copyright.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Suzuki, T., Matsuzaki, T., Hagiwara, H., Aoki, T., Takata, K.: Recent Advances in Fluorescent Labeling Techniques for Fluorescence Microscopy. Acta Histochem. Cytochem. 40(5), 131–137 (2007)
Elliot, J.T., Tona, A., Plant, A.L.: Comparison of reagents for shape analysis of fixed cells by automated fluorescence microscopy. Cytometry 52A, 90–100 (2003)
Langenbach, K.J., Elliott, J.T., Tona, A., Plant, A.L.: Evaluating the correlation between fibroblast morphology and promoter activity on thin films of extracellular matrix proteins. BMC-Biotechnology 6(1), 14 (2006)
ImageJ, public domain software, http://rsbweb.nih.gov/ij/
Rand, W.M.: Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association 66(336), 846–850 (1971)
Peskin, A.P., Dima, A., Chalfoun, J.: Predicting Segmentation Accuracy for Biological Cell Images (in process)
Peskin, A.P., Kafadar, K., Dima, A.: A Quality Pre-Processor for Biological Cells. In: 2009 International Conference on Visual Computing (2009)
Dima, A., Elliott, J.T., Filliben, J., Halter, M., Peskin, A., Bernal, J., Stotrup, B.L., Marcin, Brady, A., Plant, A., Tang, H.: Comparison of segmentation algorithms for individual cells. Cytometry Part A (in process)
Hecht, S.: The Visual Discrimination of Intensity and the Weber-Fechner Law. J. Gen. Physiol. 7(2), 235–267 (1924)
Jianhong, S.: On the foundations of visual modeling I. Weber’s law and Weberized TV restoration. Physica D 175, 241–251 (2003)
Schulman, E., Cox, C.: Misconceptions about astronomical magnitudes. Am. J. Phys. 65(10) (October 1997)
Lankton, S.: Active Contour Matlab Code Demo (2008), http://www.shawnlankton.com/2008/04/active-contour-matlab-code-demo
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chalfoun, J., Dima, A.A., Peskin, A.P., Elliott, J.T., Filliben, J.J. (2010). A Human Inspired Local Ratio-Based Algorithm for Edge Detection in Fluorescent Cell Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-17289-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17288-5
Online ISBN: 978-3-642-17289-2
eBook Packages: Computer ScienceComputer Science (R0)