ABSTRACT
The brain consists of massive nuclei with different functions. In neuroscience research, the precise recognition and delineation of nucleus boundaries is the crux of brain atlas illustration. Here, we propose a method based on Markov Random Field. This method introduces fractional differentiation into texture feature extraction and a new potential energy function is defined based on kernel region information. To handle the problem of large data volume, we propose a strategy of dual MRF processing with down sampling for pre classification. Finally, the fuzzy entropy criterion is used to optimize the segmentation results. Experiments on model data and real tissue slice data show that this method can not only segment the regions with obvious differences in the density of the nuclei with insignificant differences in the density of nuclei, but also divide the nuclei without insignificant density differences by the cell shapes and textures. In addition, this method can not only segment a specific target kernel in one calculation, but also simultaneously partition multiple target kernels. The segmentation algorithm shows great potential for illustrating high-resolution 3D brain atlases.
- Wiegell, M. R., Tuch, D. S., Larsson, H. B., & Wedeen, V. J. (2003). Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. NeuroImage, 19(2), 391-401.Google Scholar
- van der Lijn, F., 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.Google Scholar
- McDonald, A. J. (1982). Cytoarchitecture of the central amygdaloid nucleus of the rat. Journal of Comparative Neurology, 208(4), 401-418.Google Scholar
- Geisler, S., Andres, K. H., & Veh, R. W. (2003). Morphologic and cytochemical criteria for the identification and delineation of individual subnuclei within the lateral habenular complex of the rat. Journal of Comparative Neurology, 458(1), 78-97.Google Scholar
- 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.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.Google Scholar
- K. Brodmann, Beitraege zur histologischen Lokalisation der Grosshirnrinde. III Mitteilung. Die Rinenfelder der neideren Affen, J. Psychol. Neurol., 4 (1905), pp. 177–226Google Scholar
- Paxinos, G., & Franklin, K. B. (2004). The mouse brain in stereotaxic coordinates. Gulf Professional Publishing.Google Scholar
- Dong, H. W. (2008). The Allen reference atlas: A digital color brain atlas of the C57Bl/6J male mouse. John Wiley & Sons Inc.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.Google Scholar
- Li, A., Gong, H., Zhang, B., Wang, Q., Yan, C., Wu, J., ... & Luo, Q. (2010). Micro-optical sectioning tomography to obtain a high-resolution atlas of the mouse brain. Science, 330(6009), 1404-1408.Google Scholar
- Xiang, Y., Büttner-Ennever, J., Cohen, B., & Raphan, T. (2004). Texture-based approaches for identifying neuro-anatomical structures and electrode tracks. Computer methods and programs in biomedicine, 74(3), 221-233.Google Scholar
- Mesejo, P., Ugolotti, R., Cagnoni, S., Cunto, F. D., & Giacobini, M. (2012, June). Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest. In Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on(pp. 1-4). IEEE.Google Scholar
- Mesejo, P., Cagnoni, S., Costalunga, A., & Valeriani, D. (2013, July). Segmentation of histological images using a metaheuristic-based level set approach. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation (pp. 1455-1462). ACM.Google Scholar
- Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M. É., ... & Shah, N. J. (2013). BigBrain: an ultrahigh-resolution 3D human brain model. Science, 340(6139), 1472-1475.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.Google Scholar
- N. Chandgotia. Generalisation of the Hammersley-Clifford theorem on bipartite graphs [J]. Transactions of the American Mathematical Society, 2017, 369(10):7107-7137Google ScholarCross Ref
- A. D. Luca, S. Termini. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory [J]. Information & Control, 1972, 20(4):301-312.Google ScholarCross Ref
- Sonali Bhadoria, Preeti Aggarwal, C. G. Dethe, and Renu Vig, "Comparison of Segmentation Tools for Multiple Modalities in Medical Imaging," Journal of Advances in Information Technology, Vol. 3, No. 4, pp. 197-205, November, 2012.doi:10.4304/jait.3.4.197-205.Google ScholarCross Ref
- S.S. Kumar, R.S. Moni, and J. Rajeesh, "Automatic Segmentation of Liver and Tumor for CAD of Liver," Journal of Advances in Information Technology, Vol. 2, No. 1, pp. 63-70, February, 2011.doi:10.4304/jait.2.1.63-70Google ScholarCross Ref
- Farha Fatina Wahid, Raju G., Shijo M. Joseph, Debabrata Swain, Om Prakash Das, and Biswaranjan Acharya, "A Novel Fuzzy-Based Thresholding Approach for Blood Vessel Segmentation from Fundus Image," Journal of Advances in Information Technology, Vol. 14, No. 2, pp. 185-192, 2023.Google ScholarCross Ref
Index Terms
- Automatic Segmentation Method for the Nuclei Contour of High Resolution Rat Brain
Recommendations
Segmentation of cervical cell nuclei in high-resolution microscopic images
Highlights A system for semi-automatic processing of full-resolution whole-slide scans. A new algorithm for segmenting the nuclei under control of the expert user. Data storage and interaction of technical and medical experts is facilitated. Open source ...
Template method to improve brain segmentation from inhomogeneous brain magnetic resonance images at high fields
ISBI'10: Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to MacroMagnetic resonance imaging of the brain at high fields (e.g. 3T) provides high resolution and high signal to noise ratio images suitable for a wide range of clinical applications. However, radiofrequency (or B1) inhomogeneity increases with the magnetic ...
Comments