Abstract
Segmentation of cellular and nuclear boundaries in differential interference contrast microscopy images is an important pre-processing step for biological image analysis. It is considered as a challenging task because of the interference of cell walls, blurs, nonuniform intensity background, and poor contrast between the foreground and the background. In this paper, we present a novel scheme on cellular boundary segmentation. Based on shape index (SI), the proposed method focuses on the detection of cytoplasm granules inside cellular regions. With several geometric post-processing techniques, the SI thresholding results are integrated into the segmented images. Because the size of the cytoplasm granules is usually too small comparing with the thickness of focal planes in Z-stack, we can not calculate SI values according to the method of constructing the intensity isosurface in 3D images. Consequently, we regard intensity as Z coordinate and compute SI values within each slice. A computed SI represents the shape of intensity surface or the variation of intensity near to a target pixel. Furthermore, we also show the proposed method can be applied to nuclear segmentation with a different post-processing step. Experimental results show the proposed algorithm has higher accuracy than existing schemes despite the existence of cell walls with different shapes and fluctuated intensities.
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
References
Meijering, E.: Cell segmentation: 50 years down the road. IEEE Sig. Proc. Magaz. 29(5), 140–145 (2012)
Fraser, A.G., et al.: Functional genomic analysis of C. elegans chromosome I by systematic RNA interference. Nature 408, 325–330 (2000)
Yang, S.H., et al.: Phenotype analysis method for identification of gene functions involved in asymmetric division of C. elegans. J. Comput. Biol. (accepted)
Sulston, J.E., Schierenberg, E., White, J.G., Thomson, J.N.: The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev. Biol. 100(1), 64–119 (1983)
Xing, F., Yang, L.: Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review. IEEE Rev. Biomed. Eng. 9, 234–263 (2016)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation and classification in digital histopathology: A review—current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2013)
Ning, F., LeCun, Y., Piano, F., Bottou, L., Barbano, P.E.: Towards automatic phenotyping of developing embryos from videos. IEEE TIP 14(9), 1360–1371 (2005)
Bradley, D., Roth, G.: Adaptive thresholding using the integral image. J. Graph. Tools 12(2), 13–21 (2007)
Lin, G., et al.: A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry A 56A(1), 23–36 (2003)
Adiga, P.U., Chaudhuri, B.B.: An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images. PR 34(7), 1449–1458 (2001)
McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: A survey. Med. Image Anal. 1(2), 91–108 (1996)
Kyoda, K., et al.: WDDD: Worm developmental dynamics database. Nucleic Acids Res. 41(Database), 732–737 (2013)
Koenderink, A., Doom, V.: Surface shape and curvature scales. Image Vis. Comput. 10(8), 557–564 (1992)
Cantzler, H., Fisher, R.B.: Comparison of HK and SC curvature description methods. In: Proceedings of the 3rd Conference on 3D Digital Imaging and Modeling, pp. 285–291. IEEE Computer Society Press, Los Alamitos (2001)
Dorai, C., Jain, A.: COSMOS - A representation scheme for 3D free-form objects. IEEE PAMI 19(10), 1115–1130 (1997)
Huibin, L., Morvan, J., Liming, C.: 3D facial expression recognition based on histograms of surface differential quantities. In: Proceedings of the 13th International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 483–494. Springer, Heidelberg (2011)
Yang, S.H., Xu, J., Suzuki, K.: Density index: Extension of the shape index in describing local intensity variations in a 3D Image. J. Comput. Aided Des. Comput. Graph. 28(7), 1152–1159 (2016). (in Chinese)
Yoshida, H., Näppi, J.: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE TMI 20(12), 1261–1274 (2001)
Thirion, J.P., Gourdon, A.: Computing the differential characteristics of isointensity surfaces. CVIU 61(2), 190–202 (1995)
Peet, F.G., Sahota, T.S.: Surface curvature as a measure of image texture. IEEE PAMI 7(6), 734–738 (1985)
Besl, P., Jain, R.: Invariant surface characteristics for 3D object recognition in range images. CVGIP 33(1), 33–80 (1986)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE TIP 19(12), 3243–3254 (2010)
Han, X.H., Tohsato, Y., Kyoda, K., Onami, S., Nishigawa, I., Chen, Y.W.: Nuclear detection in 4D microscope images of a developing embryo using an enhanced probability map of top-ranked intensity-ordered descriptors. IPSJ Trans. Comput. Vis. Appl. 8(8), 1–15 (2016)
Acknowledgment
This work was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 16H01436. The authors wish to thank Prof. Nishigawa, Dr. Onami, Dr. Tohsato and Dr. Kyoda for their advice in this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Yang, SH., Han, XH., Chen, YW. (2018). Automatic Segmentation of Cellular/Nuclear Boundaries Based on the Shape Index of Image Intensity Surfaces. In: Chen, YW., Tanaka, S., Howlett, R., Jain, L. (eds) Innovation in Medicine and Healthcare 2017. KES-InMed 2018 2017. Smart Innovation, Systems and Technologies, vol 71. Springer, Cham. https://doi.org/10.1007/978-3-319-59397-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-59397-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59396-8
Online ISBN: 978-3-319-59397-5
eBook Packages: EngineeringEngineering (R0)