Abstract
The segmentation of brain magnetic resonance (MR) images can provide more detailed anatomical information, which can be of great help for the proper diagnosis of brain diseases. Therefore, the study of medical image segmentation technology is crucial and necessary. Owing to the presence of equipment noise and the complexity of the brain structure, the existing methods have various shortcomings and their performances are not ideal. In this study, we propose a new method based on back propagation (BP) neural networks and the AdaBoost algorithm. The BP neural network that we created has a 1–7-1 structure. We trained the system using a gravitational search algorithm. (In this algorithm, we use segmented images, which were obtained by state-of-the-art methods, as ideal output data.) Based on this, we established and trained 10 groups of back propagation neural networks (BPNNs) by applying 10 groups of different data. Subsequently, we adopted the AdaBoost algorithm to obtain the weight of each BPNN. Finally, we updated the BPNNs by training the gravitational search and AdaBoost algorithms. In this experiment, we used one group of brain magnetic resonance imaging (MRI) datasets. A comparison with four state-of-the-art segmentation methods through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results for brain MR image segmentation.
Similar content being viewed by others
References
Pham, D. L., Chenyang, X., & Prince, J. L. (2000). Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2(1), 315–337.
Shen, S., et al. (2005). MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Transactions on Information Technology in Biomedicine, 9(3), 459–467.
Rusinek, H., et al. (1991). Alzheimer disease: Measuring loss of cerebral gray matter with MR imaging. Radiology, 178(1), 109–114.
Narr, K. L., Thompson, P. M., Szeszko, P., Robinson, D., Jang, S., Woods, R. P., Kim, S., Hayashi, K. M., Asunction, D., Toga, A. W., & Bilder, R. M. (2004). Regional specificity of hippocampal volume reductions in first-episode schizophrenia. Neuroimage, 21(4), 1563–1575.
Ji, Z., Xia, Y., Sun, Q., Chen, Q., Xia, D., & Feng, D. D. (2012). Fuzzy local Gaussian mixture model for brain MR image segmentation. IEEE Transactions on Information Technology in Biomedicine, 16(3), 339–347.
Balafar, M. A., et al. (2010). Review of brain MRI image segmentation methods. Artificial Intelligence Review, 33(3), 261–274.
Suzuki, H., & Toriwaki, J.-i. (1991). Automatic segmentation of head MRI images by knowledge guided thresholding. Computerized Medical Imaging and Graphics, 15(4), 233–240.
Verma, H., Agrawal, R. K., & Sharan, A. (2016). An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation. Applied Soft Computing, 46, 543–557.
Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence, 6, 583–598.
Li, N., Liu, M., & Li, Y. (2007). Image segmentation algorithm using watershed transform and level set method, acoustics, speech and signal processing, ICASSP 2007. IEEE International Conference on, Vol. 1. IEEE, 2007.
Held, K., et al. (1997). Markov random field segmentation of brain MR images. IEEE Transactions on Medical Imaging, 16(6), 878–886.
Rohlfing, T., et al. (2004). Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage, 21(4), 1428–1442.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.
Tran, D., & Wagner, M. (2000). Fuzzy entropy clustering, Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on, Vol. 1. IEEE.
Verma, H., Agrawal, R. K., & Kumar, N. (2014). Improved fuzzy entropy clustering algorithm for MRI brain image segmentation. International Journal of Imaging Systems and Technology, 24(4), 277–283.
Krinidis, S., & Chatzis, V. (2010). A robust fuzzy local information C-means clustering algorithm. IEEE Transactions on Image Processing, 19(5), 1328–1337.
Adhikari, S. K., et al. (2015). Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images. Applied Soft Computing, 34, 758–769.
Xu, Z., & Junjie, W. (2010). Intuitionistic fuzzy C-means clustering algorithms. Journal of Systems Engineering and Electronics, 21(4), 580–590.
Silva Filho, T. M., et al. (2015). Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Systems with Applications, 42(17-18), 6315–6328.
Pham, T. X., Siarry, P., & Oulhadj, H. (2018). Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Applied Soft Computing, 65, 230–242.
Moeskops, P., et al. (2017). Adversarial training and dilated convolutions for brain MRI segmentation. In Deep learning in medical image analysis and multimodal learning for clinical decision support (pp. 56–64). Cham: Springer.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.
Duman, S., et al. (2012). Optimal power flow using gravitational search algorithm. Energy Conversion and Management, 59, 86–95.
Shuaib, Y. M., Kalavathi, M. S., & Rajan, C. C. A. (2015). Optimal capacitor placement in radial distribution system using gravitational search algorithm. International Journal of Electrical Power & Energy Systems, 64, 384–397.
Mirjalili, S., Hashim, S. Z. M., & Sardroudi, H. M. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Applied Mathematics and Computation, 218, 22, 11125–11137.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533.
Ding, S., Chunyang, S., & Junzhao, Y. (2011). An optimizing BP neural network algorithm based on genetic algorithm. Artificial Intelligence Review, 36(2), 153–162.
Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2011). Filter modeling using gravitational search algorithm. Engineering Applications of Artificial Intelligence, 24(1), 117–122.
González, B., et al. (2015). Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition. Expert Systems with Applications, 42(14), 5839–5847.
Sun, G., et al. (2018). A stability constrained adaptive alpha for gravitational search algorithm. Knowledge-Based Systems, 139, 200–213.
Chao, Z., Kim, D., & Kim, H.-J. (2018). Multi-modality image fusion based on enhanced fuzzy radial basis function neural networks. Physica Medica, 48, 11–20.
Freund, Y., et al. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933–969.
Li, N., et al. (2013). Recognizing human actions by BP-AdaBoost algorithm under a hierarchical recognition framework, acoustics, speech and signal processing (ICASSP), 2013 IEEE International Conference on, IEEE.
Zheng, Y. L., et al. (2018). A novel fault diagnosis method for photovoltaic array based on BP-AdaBoost strong classifier. IOP Conference Series: Earth and Environmental Science, 188(1). IOP Publishing.
BrainWeb [Online]. Available: www.bic.mni.mcgill.ca/brainweb/
Saha, R., Bajger, M., & Lee, G.. (2016). Spatial shape constrained fuzzy C-means (FCM) clustering for nucleus segmentation in pap smear images. Digital Image Computing: Techniques and Applications (DICTA), 2016 International Conference on, IEEE.
Acknowledgments
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017R1A2B2001818).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chao, Z., Kim, HJ. Brain Image Segmentation Based on the Hybrid of Back Propagation Neural Network and AdaBoost System. J Sign Process Syst 92, 289–298 (2020). https://doi.org/10.1007/s11265-019-01497-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11265-019-01497-y