Non-parametric Brain Tissues Segmentation via a Parallel Architecture of CNNs
A fully automatic brain tissue segmentation framework is introduced in current paper, it is based on a parallel architecture of a specialized convolutional deep neuronal network designed to develop binary segmentation. The main contributions of this proposal imply its ability to segment brain RMI images of different acquisition modes, it does not require the initialization of any parameter; apart from the foregoing, it does not require any preprocessing stage to improve the quality of each slice. Experimental tests were developed considering BrainWeb and BraTS 2017 databases. The robustness and effectiveness of this proposal is verified by quantitative and qualitative results.
KeywordsBrain RMI segmentation Parallel architecture Convolutional deep neuronal network
The authors are grateful to CONACYT, as well as Tecnológico Nacional de México/CENIDET for their support trough the project 5628.19-P so-called “Sistema embebido para asistencia de conducción basado en Lógica Difusa Tipo-2”.
- 1.Angulakshmi, M., Priya, G.L.: Brain tumour segmentation from MRI using superpixels based spectral clustering. J. King Saud Univ.-Comput. Inf. Sci. (2018). https://www.sciencedirect.com/science/article/pii/S1319157817303476
- 7.Narayanan, A., Rajasekaran, M.P., Zhang, Y., Govindaraj, V., Thiyagarajan, A.: Multi-channeled MR brain image segmentation: a novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern. Biomed. Eng. 39(2), 350–381 (2018)CrossRefGoogle Scholar
- 9.Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRefGoogle Scholar
- 10.Senthilkumar, C., Gnanamurthy, R.: A fuzzy clustering based MRI brain image segmentation using back propagation neural networks. Cluster Comput., 1–8 (2018). https://link.springer.com/article/10.1007/s10586-017-1613-x
- 12.Vedaldi, A., Lenc, K., Ehrhardt, S., Jaderberg, M.: MatConvNet: CNNs for MATLAB (2014)Google Scholar
- 13.Brain Web: Simulated brain database. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill (2004). http://brainweb.bic.mni.mcgill.ca/brainweb