Abstract
This paper presents an original method for splitting overlapped cells in microscopical images, based on a template matching strategy. First,a single template cell is estimated using an Expectation Maximization algorithm applied to a collection of correctly segmented cells from the original image. Next, a process based on matching the template against the clumped shape and removing the matched area is applied iteratively. A chain code representation is used for establishing best correlation between these two shapes. Maximal correlation point is used as an landmark point for the registration approach, which finds the affine transformation that maximises the intersection area between both shapes. Evaluation was carried out on 18 images in which 52 clumped shapes were present. The number of found cells was compared with the number of cells counted by an expert and results show agreement on a \(93\:\%\) of the cases.
Chapter PDF
Similar content being viewed by others
References
Ong, S.H., Jayasooriah, Y.H.H., Sinniah, R.: Decomposition of digital clumps into convex parts by contour tracing and labelling. Pattern Recognition Letters 13, 789–795 (1992)
Bowie, J., Young, I.: An analysis technique for biological shape-ii. Acta Cytologica 21, 455–464 (1977)
Kumar, S., Ong, S., Ranganath, S., Ong, T., Chew, F.: Arule based approach for robust clump splitting. Pattern Recognition 39, 1088–1098 (2006)
Hall, R., Malia, R.G.: Medical Laboratory Haematology, 2nd edn. Butterworth-Heinemann Ltd. (1991)
Wang, Z., Ben-Arie, J.: Model based segmentation and detection of affine transformed shapes in cluttered images. In: International Conference on Image Processing (1998)
Gadkari, M.S., Refai, H.H., Sluss, J.J., Broughan, T.A., Broughan, T.A., Naukam, R.: The detection of single hepatocytes within clusters in microscopic images. In: Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (2004)
Wurflinger, T., Stockhausen, J., Meyer-Ebrecht, D., Bocking, A.: Robust automatic coregistration, segmentation, and classification of cell nuclei in multimodal cytopathological microscopic images. Computerized Medical Imaging and Graphics 28, 87–98 (2004)
di Ruberto, C., Dempster, A., Khan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image and Vision Computing 20(2), 133–146 (2002)
Ross, N.E., Pritchard, C.J., Rubin, D.M., Dusï, A.G.: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing 44, 427–436 (2006)
Nilsson, B., Heyden, A.: Segmentation of dense leukocyte clusters. In: Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (2001)
Althoff, K.: Implementation and evaluation of stem cell segmentation techniques. Technical report, Chalmers University of Technology (2003)
Liu, L., Sclaroff, S.: Deformable model guided region split and merge of image regions. Image and Vision Computing 22, 343–354 (2004)
Poon, S.S.S., Ward, R.K., Palcic, B.: Automated image detection and segmentation in blood smears. Cytometry 13, 766–774 (1992)
Junqueira, L.C., Carneiro, J.: Basic Histology, 10th edn. MacGraw Hill, New York (2003)
Gonzlez, R.C., Woods, R.E.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (2002)
Braga-Neto, U.: Multiscale connected operators. Journal of Mathematical Imaging and Vision 22, 199–216 (2005)
Warfield, S.K., Zou, K.H., Wells, W.M.: Simultaneous truth and performance level estimation (staple): An algorithm for the validation of image segmentation. IEEE Transactions On Medical Imaging 23, 903–921 (2004)
Dempster, A., Laird, N., Rubin, D.: Maximum-likelihood form incomplete data via the em algorithm. Journal of Royal Statistical Society 39, 34–37 (1977)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Díaz, G., Gonzalez, F., Romero, E. (2007). Automatic Clump Splitting for Cell Quantification in Microscopical Images. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_79
Download citation
DOI: https://doi.org/10.1007/978-3-540-76725-1_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76724-4
Online ISBN: 978-3-540-76725-1
eBook Packages: Computer ScienceComputer Science (R0)
