Abstract
Image segmentation remains an ongoing challenge in medical image processing research. Owing to brain tumor’s inhomogeneous structure and blurred boundary, the segmentation of brain tumor image is not always ideal. Therefore, we propose a novel region growing model that enables to segment the brain tumor image accurately and automatically. The model mainly improves the selection of seed points and the growth rules. Using the method of fusion information with multimodal MRI images is described to select the seed point automatically, which makes the segmentation algorithm more robust. Furthermore, in order to mostly remain the local feature and the boundary information of brain tumor, a spatial texture feature is constructed in this study. Based on the above model, an automatic brain tumor image segmentation algorithm is established, which uses the region growing with the Co-constraint of intensity and spatial texture. In terms of performance evaluation, the proposed method not only outperforms other segmentation algorithms in the accuracy of results, but also has lower computational cost. This is undoubtedly a worthy method of brain tumor image segmentation.
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References
Arabi, H.: Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med. Image Anal. 31, 1 (2016)
Abdel-Maksoud, E.: Brain tumor segmentation based on a hybrid clustering technique. Egypt. Inform. J. 16(1), 71–81 (2015)
Feng, Y., Shen, X., Chen, H., Zhang, X.: Internal generative mechanism based Otsu multilevel thresholding segmentation for medical brain images. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 3–12. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24075-6_1
Salman, Y.: Validation techniques for quantitative brain tumors measurements. In: 2005 IEEE Engineering in Medicine and Biology 27th annual Conference, pp. 7048. IEEE, Shanghai (2006)
Thiruvenkadam, K.: Brain tumor segmentation of MRI brain images through FCM clustering and seeded region growing technique. Int. J. Appl. Eng. Res. 10(76), 427–432 (2015)
Sarathi, M.P.: Automated brain tumor segmentation using novel feature point detector and seeded region growing. In: 2013 36th International Conference on Telecommunications and Signal Processing, pp. 648–652. IEEE, Rome (2013)
Ho, Y.L., Lin, W.Y., Tsai, C.L., et al.: Automatic brain extraction for T1-weighted magnetic resonance images using region growing. In: 2016 16th International Conference on Bioinformatics and Bioengineering, pp. 250–253. IEEE, Taichung (2016)
Suri, J.S., Wilson, D., Laxminarayan, S.: Handbook of Biomedical Image Analysis. Springer, New York (2005). https://doi.org/10.1007/b104805
Jafari, M.: Automatic brain tissue detection in MRI images using seeded region growing segmentation and neural network classification. Aust. J. Basic Appl. Sci. 34(1), 577–582 (2011)
Viji, K.S.A.: Modified texture based region growing segmentation of MR brain images. In: 2013 IEEE Conference on Information and Communication Technologies, pp. 691–695. IEEE, Thuckalay (2013)
Charutha, S.: An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, pp. 1193–1199. IEEE, Kanyakumari (2014)
Viji, A.: Modified texture, intensity and orientation constraint based region growing segmentation of 2D MR brain tumor images. Int. Arab J. Inf. Technol. 13(6A), 723–731 (2016)
Havaei, M.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2017)
Nabizadeh, N.: Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm. Expert Syst. Appl. 77, 1–10 (2017)
Hamamci, A.: Tumor-cut: segmentation of brain tumors on contrast enhanced MR images for radiosurgery applications. IEEE Trans. Med. Imaging 31(3), 790 (2012)
Sompong, C.: An efficient brain tumor segmentation based on cellular automata and improved tumor-cut algorithm. Expert Syst. Appl. 72, 231–244 (2017)
DTU Compute. https://www.imm.dtu.dk/projects/BRATS-2012/data.html
Acknowledgement
This research is supported by the National Natural Science Foundation of China (61672259, 61602203), Key Projects of Jilin Province Science and Technology Development Plan (20180201064SF), and Outstanding Young Talent Foundation of Jilin Province (20170520064JH, 20180520020JH).
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Cui, S., Shen, X., Lyu, Y. (2019). Automatic Segmentation of Brain Tumor Image Based on Region Growing with Co-constraint. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_50
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