Abstract
Segmentation of neural cells in three dimensional fluorescence microscopy images is a challenging image processing problem. In addition to being important to neurobiologists, accurate segmentation is a vital component of an automated image processing system. Due to the complexity of the data, particularly the extreme irregularity in neural cell shape, generic segmentation techniques do not perform well. This paper presents a novel segmentation technique for segmenting neural cells in three dimensional images. Accuracy rates of over 90% are reported on a data set of 100 images containing over 130 neural cells and subsequently validated using a novel data set of 64 neurons.
This is a preview of subscription content, log in via an institution to check access.
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
Alavi, A., Cavanagh, B., Tuxworth, G., Meedeniya, A., Mackay-Sim, A., Blumenstein, M.: Automated classification of dopaminergic neurons in the rodent brain. Presented at the International Joint Conference on Neural Networks, Atlanta (2009)
Fernandez, G., Zyrd, J.P., Kunt, M.: A New Plant Cell Image Segmentation Algorithm. Presented at the ICIAP (1995)
Jarkrans, T.: Algorithms for Cell Image Analysis in Cytology and Pathology. Faculty of Science and Technology, Uppsala University, Sweden (1996)
Byriel, J.: Neuro-Fuzzy Classification of Cells in Cervical Smears. Technical University of Denmark, Lyngby (1999)
Nguyen, M.N., Cavanagh, B., Davenport, T., Norazit, A., Meedeniya, A.: Tissue processing for epifluoresence microscopy. In: Méndez-Vilas, A., Diaz, J. (eds.) Microscopy: Science, Technology, Applications and Education, vol. 4, Formatex, Badajoz (2010)
Schaefer, L.H., Schuster, D., Herz, H.: Generalized approach for accelerated maximum likelihood based image restoration applied to three-dimensional fluorescence microscopy. Journal of Microscopy 204, 99–107 (2001)
Murray, J.M., Appleton, P.L., Swedlow, J.R., Waters, J.C.: Evaluating performance in three-dimensional fluorescence Microscopy. Journal of Microscopy 228, 390–405 (2007)
Benlamri, R.: Range image segmentation of scenes with occluded curved objects. Pattern Recognition Letters 21, 1051–1060 (2000)
Jiang, X., Bunke, H., Meier, U.: High level feature based range image segmentation. Image and Vision Computation 18, 817–822 (2000)
Yu, W., et al.: Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres. In: Bebis, G., et al. (eds.) Advances in Visual Computing, vol. 5875, pp. 531–543. Springer, Heidelberg (2009)
Long, F., et al.: Automatic Segmentation of Nuclei in 3D Microscopy Images of C.Elegans. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007, pp. 536–539 (2007)
Cai, H., et al.: Using nonlinear diffusion and mean shift to detect and connect cross-sections of axons in 3D optical microscopy images. Medical Image Analysis 12 (2008)
Yu, W., et al.: Segmentation of Neural Stem/Progenitor Cells Nuclei within 3-D Neurospheres. In: Advances in Visual Computing, pp. 531–543 (2009)
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)
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
Tuxworth, G., Meedeniya, A., Blumenstein, M. (2010). Segmentation of Inter-neurons in Three Dimensional Brain Imagery. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2010. Lecture Notes in Computer Science, vol 6474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17688-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-17688-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17687-6
Online ISBN: 978-3-642-17688-3
eBook Packages: Computer ScienceComputer Science (R0)