Skip to main content

Advertisement

SpringerLink
Log in

Automated Brain Region Segmentation for Single Cell Resolution Histological Images Based on Markov Random Field

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

The brain consists of massive regions with different functions and the precise delineation of brain region boundaries is important for brain region identification and atlas illustration. In this paper we propose a hierarchical Markov random field (MRF) model for brain region segmentation, where a MRF is applied to the downsampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fractional differential feature and a gray level co-occurrence matrix are extracted as the observed vector for the MRF and a new potential energy function, which can capture the spatial characteristic of brain regions, is proposed as well. A fuzzy entropy criterion is used to fine-tune the boundary from the hierarchical MRF model. We test the model both on synthetic images and real histological mouse brain images. The result suggests that the model can accurately identify target regions and even the whole mouse brain outline as a special case. An interesting observation is that the model cannot only segment regions with different cell density but also can segment regions with similar cell density and different cell morphology texture. Thus this model shows great potential for building the high-resolution 3D brain atlas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Balafar, M. A., Ramli, A. R., Saripan, M. I., & Mashohor, S. (2010). Review of brain MRI image segmentation methods. Artificial Intelligence Review, 33(3), 261–274.

    Article  Google Scholar 

  • Brodmann, K. (1908). Beiträge zur histologischen lokalisation der groβhirnrinde. Journal für Psychologie und Neurologie, 10, 231–246.

    Google Scholar 

  • Brunjes, P. C., Illig, K. R., & Meyer, E. A. (2005). A field guide to the anterior olfactory nucleus (cortex). Brain Research Reviews, 50(2), 305–335.

    Article  Google Scholar 

  • Chandgotia, & Nishant. (2017). Generalisation of the Hammersley-Clifford theorem on bipartite graphs. Transactions of the American Mathematical Society, 369(10), 7107–7137.

    Article  Google Scholar 

  • David, S. A., Linares, J. L., & Pallone, E. M. (2011). Fractional order calculus: Historical apologia, basic concepts and some applications. Revista Brasileira de Ensino de Física, 33(4), 4302–4302.

    Article  Google Scholar 

  • De Luca, A., & Termini, S. (1972). A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control, 20(4), 301–312.

    Article  Google Scholar 

  • Der Lijn, F. V., Den Heijer, T., Breteler, M. M., & Niessen, W. J. (2008). Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. Neuroimage, 43(4), 708–720.

    Article  Google Scholar 

  • Derin, H., Elliott, H., Cristi, R., & Geman, D. (1984). Bayes smoothing algorithms for segmentation of binary images modeled by Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 707–720.

    Article  CAS  Google Scholar 

  • Dong, H. W. (2008). The Allen reference atlas: A digital color brain atlas of the C57Bl/6J male mouse. Wiley..

  • Dorr, A. E., Lerch, J. P., Spring, S., Kabani, N., & Henkelman, R. M. (2008). High resolution three-dimensional brain atlas using an average magnetic resonance image of 40 adult C57Bl/6J mice. NeuroImage, 42(1), 60–69.

    Article  CAS  Google Scholar 

  • Economo, M. N., Clack, N. G., Lavis, L. D., Gerfen, C. R., Svoboda, K., Myers, E. W., & Chandrashekar, J. (2016). A platform for brain-wide imaging and reconstruction of individual neurons. Elife, 5, e10566.

    Article  Google Scholar 

  • Feng, Z., Li, A., Gong, H., & Luo, Q. (2016). An automatic method for nucleus boundary segmentation based on a closed cubic spline. Frontiers in Neuroinformatics, 10, 21.

    Article  Google Scholar 

  • Franklin, K. B. J., & Paxinos, G. (2004). The mouse brain: In stereotaxic coordinates. Rat Brain in Stereotaxic Coordinates, 3(2), 6.

    Google Scholar 

  • Gahr, M. (1997). How should brain nuclei be delineated? Consequences for developmental mechanisms and for correlations ofarea size, neuron numbers and functions of brain nuclei. Trends in Neurosciences, 20(2), 58–62.

    Article  CAS  Google Scholar 

  • Gong, H., Zeng, S., Yan, C., Lv, X., Yang, Z., Xu, T., Feng, Z., Ding, W., Qi, X., Li, A., & Wu, J. (2013). Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution. Neuroimage, 74, 87–98.

    Article  Google Scholar 

  • Gong, H., Xu, D., Yuan, J., Li, X., Guo, C., Peng, J., Li, Y., Schwarz, L. A., Li, A., Hu, B., & Xiong, B. (2016). High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nature Communications, 7, 12142.

    Article  CAS  Google Scholar 

  • Gonzalez, R. C., Woods R. E., & Eddins S. L. (2004). Digital image processing using Matlab. Pearson Prentice Hall.

    Google Scholar 

  • Gottsegen, C. J., Merkle, A. N., Bencardino, J. T., & Gyftopoulos, S. (2017). Advanced MRI techniques of the shoulder joint: Current applications in clinical practice. American Journal of Roentgenology, 209(3), 544–551.

    Article  Google Scholar 

  • Guo, C., Peng, J., Zhang, Y., Li, A., Li, Y., Yuan, J., Xu, X., Ren, M., Gong, H., & Chen, S. (2017). Single-axon level morphological analysis of corticofugal projection neurons in mouse barrel field. Scientific Reports, 7(1), 2846.

    Article  Google Scholar 

  • Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621.

    Article  Google Scholar 

  • Johnson, G. A., Badea, A., Brandenburg, J., Cofer, G., Fubara, B., Liu, S., & Nissanov, J. (2010). Waxholm space: An image-based reference for coordinating mouse brain research. Neuroimage, 53(2), 365–372.

    Article  Google Scholar 

  • Kemper, V. G., De Martino, F., Emmerling, T. C., Yacoub, E., & Goebel, R. (2018). High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4 T. Neuroimage, 164, 48–58.

    Article  Google Scholar 

  • Li, A., Gong, H., Zhang, B., Wang, Q., Yan, C., Wu, J., Liu, Q., Zeng, S., & Luo, Q. (2010). Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science, 330(6009), 1404–1408.

    Article  CAS  Google Scholar 

  • Li, Y., Gong, H., Yang, X., Yuan, J., Jiang, T., Li, X., Sun, Q., Zhu, D., Wang, Z., Luo, Q., & Li, A. (2017). TDat: An efficient platform for processing petabyte-scale whole-brain volumetric images. Frontiers in Neural Circuits, 11, 51.

    Article  Google Scholar 

  • Maksimovic, R., Stankovic, S., & Milovanovic, D. (2000). Computed tomography image analyzer: 3D reconstruction and segmentation applying active contour models—‘snakes’. International Journal of Medical Informatics, 58, 29–37.

    Article  Google Scholar 

  • Marx, V. (2012). High-throughput anatomy: Charting the brain's networks. Nature, 490(7419), 293–298.

    Article  CAS  Google Scholar 

  • Mesejo, P., Ugolotti, R., Cagnoni, S., Di Cunto, F., & Giacobini, M. (2012). Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest. In 2012 25th IEEE International Symposium on Computer-Based Medical Systems (pp. 1–4).

    Google Scholar 

  • Mesejo, P., Cagnoni, S., Costalunga, A., & Valeriani, D. (2013). Segmentation of histological images using a metaheuristic-based level set approach. In Genetic and Evolutionary Computation Conference Companion (pp. 1455–1462).

    Google Scholar 

  • Meyer, E. A., Illig, K. R., & Brunjes, P. C. (2006). Differences in chemo-and cytoarchitectural features within pars principalis of the rat anterior olfactory nucleus suggest functional specialization. Journal of Comparative Neurology, 498(6), 786–795.

    Article  Google Scholar 

  • Mirzapour, F., & Ghassemian, H. (2013). Using GLCM and Gabor filters for classification of PAN images. In 2013 21st Iranian Conference on Electrical Engineering (pp. 1–6).

    Google Scholar 

  • O'Rahilly, R., & Müller, F. (1983). Basic human anatomy: A regional study of human structure (p. 566). Philadelphia: Saunders.

    Google Scholar 

  • Serrano, C., & Acha, B. (2009). Pattern analysis of dermoscopic images based on markov random fields. Pattern Recognition, 42(6), 1052–1057.

    Article  Google Scholar 

  • Umaselvi, M., Kumar, S. S., & Athithya, M. (2012). Color based urban and agricultural land classification by GLCM texture features. In IET Chennai 3rd International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2012).

    Google Scholar 

  • Wu, J., He, Y., Yang, Z., Guo, C., Luo, Q., Zhou, W., Chen, S., Li, A., Xiong, B., Jiang, T., & Gong, H. (2014). 3D BrainCV: Simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with one-micron voxel resolution. Neuroimage, 87, 199–208.

    Article  Google Scholar 

  • Xiong, B., Li, A., Lou, Y., Chen, S., Long, B., Peng, J., Yang, Z., Xu, T., Yang, X., Li, X., & Jiang, T. (2017). Precise cerebral vascular atlas in stereotaxic coordinates of whole mouse brain. Frontiers in Neuroanatomy, 11, 128.

    Article  Google Scholar 

  • Yousif, O., & Ban, Y. (2014). Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4288–4300.

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the 973 projection (Grant No.2015CB755602), Science Fund for Creative Research Group of China (Grant No.61721092) and National Natural Science Foundation of China (Grant No. 91749209). We appreciate Shangbin Chen, Chaozhen Tan and Hong Ning for constructive suggestions, Wu Chen and Zhenyu Pan for image analysis.

Author information

Authors and Affiliations

  1. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Xiaofeng Xu, Yue Guan, Hui Gong, Zhao Feng, Wenjuan Shi, Anan Li, Miao Ren, Jing Yuan & Qingming Luo

  2. MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China

    Xiaofeng Xu, Yue Guan, Hui Gong, Zhao Feng, Wenjuan Shi, Anan Li, Miao Ren, Jing Yuan & Qingming Luo

  3. HUST-Suzhou Institute for Brainsmatics, Suzhou, China

    Hui Gong, Anan Li & Jing Yuan

Authors
  1. Xiaofeng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhao Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenjuan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Miao Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jing Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qingming Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yue Guan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, X., Guan, Y., Gong, H. et al. Automated Brain Region Segmentation for Single Cell Resolution Histological Images Based on Markov Random Field. Neuroinform 18, 181–197 (2020). https://doi.org/10.1007/s12021-019-09432-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-019-09432-z

Keywords

Search

Navigation