General Purpose Segmentation for Microorganisms in Microscopy Images

S. N. Jensen, R. Irani, T. B. Moeslund, Christian Rankl

Abstract

In this paper, we propose an approach for achieving generalized segmentation of microorganisms in microscopy images. It employs a pixel-wise classification strategy based on local features. Multilayer perceptrons are utilized for classification of the local features and is trained for each specific segmentation problem using supervised learning. This approach was tested on five different segmentation problems in bright field, differential interference contrast, fluorescence and laser confocal scanning microscopy. In all instance good results were achieved with the segmentation quality scoring a Dice coefficient of 0.831 or higher.

References

  1. Ao, J., Mitra, S., Long, R., Nutter, B., and Antani, S. (2011). A hybrid watershed method for cell image segmentation. IEEE Southwest Symposium on Image Analysis and Interpretation.
  2. Ariel J. Bernal, S. E. F. and Bernal, L. J. (2008). Cell recognition using wavelet templates. Canadian Conference on Electrical and Computer Engineering.
  3. bin Abdul Jamil, M. M., Sharif, J. M., Miswan, M. F., Ngadi, M. A., and Salam, M. S. H. (2012). Red blood cell segmentation using masking and watershed algorithm: A preliminary study. International Conference on Biomedical Engineering.
  4. Brain, D. (2003). Learning From Large Data: Bias, Variance, Sampling and Learning Curves. PhD thesis, Deakin University.
  5. Carpenter, A., Jones, T., Lamprecht, M., and et al (2006). CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biology.
  6. Cheng, J. and Rajapakse, J. C. (2009). Segmentation of clustered nuclei with shape markers and marking function. IEEE Transactions on Biomedical Engineering.
  7. F. Boray Tek, A. G. D. and Kale, I. (2009a). Computer Vision for Microscopy Diagnosis of Malaria. Malaria Journal.
  8. F. Boray Tek, A. G. D. and Kale, I. (2009b). Malaria Parasite Detection in Peripheral Blood Images. IEEE International Conference on Acoustics, Speech and Signal Processing.
  9. for Bio-Image Informatics, C. (2013). Ucsb biosegmentation benchmarking.
  10. Institute, B. (2013). Broad bioimage benchmark collection.
  11. Kane, C., Iwasa, J., Orloff, D., and Wong, W. (2013). The cell: An image library.
  12. Kevin Smith, A. C. and Lepetit, V. (2009). Fast ray features for learning irregular shapes. Internation Conference on Computer Vision.
  13. Kujiper, A. and Heise, B. (2008). An automated cell segmentation method for differential interference contrast microscopy. International Conference on Pattern Recognition.
  14. Lebrun, G., Charrier, C., Lezoray, O., Meurie, C., and Cardot, H. (2007). A Fast And Efficient Segmentation Scheme For Cell Microscopic Image. Cellular and Molecular Biology.
  15. Meijering, E. (2012). Cell segmentation: 50 years down the road. IEEE Signal Processing Magazine.
  16. Nawi, N. M., Ransing, M. R., and Ransing, R. S. (2006). An improved learning algorithm based on the broydenfletcher-goldfarb-shanno (bfgs) method for back propagation neural networks. International Conference on Intelligent Systems Design and Applications.
  17. Wienert, S., Heim, D., Saeger, K., Stenzinger, A., Beil, M., Hufnagl, P., Dietel, M., Denkert, C., and Klauschen, F. (2012). Detection and segmentation of cell nuclei in virtual microscopy images; a minimum-model approach. Scientific Reports.
  18. Zaritsky, A., Natan, S., Horev, J., Hecht, I., Wolf, L., BenJacob, E., and Tsarfaty, I. (2011). Cell motility dynamics: A novel segmentation algorithm to quantify multi-cellular bright field microscopy images. PLoS ONE.
  19. Zhaozhen Ying, Ryoma Bise, M. C. and Kanade, T. (2010). Cell segmentation in microscopy imagery using a bag of local bayesian classifiers. The IEEE International Symposium on Biomedical Imaging.
  20. Zhou, Y. (2007). Cell segmentation using level set method. Technical report, Institute for Computational and Applied Mathematics, Johannes Kepler University, Linz.
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Paper Citation


in Harvard Style

Jensen S., Irani R., Moeslund T. and Rankl C. (2014). General Purpose Segmentation for Microorganisms in Microscopy Images . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 690-695. DOI: 10.5220/0004827106900695


in Bibtex Style

@conference{visapp14,
author={S. N. Jensen and R. Irani and T. B. Moeslund and Christian Rankl},
title={General Purpose Segmentation for Microorganisms in Microscopy Images},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={690-695},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004827106900695},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - General Purpose Segmentation for Microorganisms in Microscopy Images
SN - 978-989-758-003-1
AU - Jensen S.
AU - Irani R.
AU - Moeslund T.
AU - Rankl C.
PY - 2014
SP - 690
EP - 695
DO - 10.5220/0004827106900695