Abstract
Brain tumor due to their increasing rate and high uncertainty has become a curse for mankind. For their effective diagnosis, automatic systems called Computed Aided Diagnosis (CAD) systems have developed that help in tumor analysis without manual interference. However, due to high variability in tumors, their segmentation from MR images is a challenging task. This paper proposes an improved tumor segmentation methodology that is an extension to simple thresholding technique. In this method, different regions of the binary images are labeled and are segregated on the basis of solidity and area. Then the region having solidity around 50% and maximum area is extracted as tumor. This segmented region is further dilated to include edema tissues surrounding the tumor. The performance of the methodology is justified by the results obtained in which only the tumorous region has been extracted indicating successful segmentation without inclusion of other brain tissues.
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References
Mohana, G., Subashini, M.M.: MRI based medical image analysis: survey on brain tumor grade classification. Biomed. Signal Process. Control 39, 139–161 (2019)
Bhateja, V., Misra, M., Urooj, S.: Computer-aided analysis of mammograms. In: Non-Linear Filters for Mammogram Enhancement, pp. 21–27. Springer, Singapore (2020)
Basavaraju, H.T., et al.: Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model. Evolut. Intell. 1–14 (2020)
Bhadauria, A.S., Bhateja, V., Nigam, M., Arya, A.: Skull stripping of brain MRI using mathematical morphology. In: Smart Intelligent Computing and Applications, pp. 775–780. Springer, Singapore (2020)
Bhateja, V., et al.: Two-stage multi-modal MR images fusion method based on parametric logarithmic image processing (PLIP) model. Pattern Recogn. Lett. (2020)
Tian, G., Xia, Y., Zhang, Y., Feng, D.: Hybrid genetic and variational expectation-maximization algorithm for Gaussian-mixture-model-based brain MR image segmentation. IEEE Trans. Inf. Technol. Biomed. 15(3), 373–380 (2011)
Zhang, N., Ruan, S., Lebonvallet, S., Liao, Q., Zhu, Y.: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115, 256–269 (2011)
Ji, Z., Suna, Q., Xiab, Y., Chena, Q., Xiaa, D., Feng, D.: Generalized rough fuzzy C-means algorithm for brain MR image segmentation. Comput. Methods Programs Biomed. 108, 644–655 (2011)
Mohsen, H., El-Dahshan, E.A., Salem, A.M.: A machine learning technique for MRI brain images. In: Proceedings of 8th IEEE Conference on Informatics and Systems, pp. 161–16. Cairo, Egypt (2012)
Somasundaram, K., Kalaiselvi, T.: Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations. Comput. Biol. Med. 41(8), 716–725 (2011)
Radhi, A.A.: Efficient algorithm for the detection of a brain tumor from an MRI images. Int. J. Comput. Appl. 170(10), 38–42 (2017)
Laddha, R.R., Ladhake, S.A.: A review on brain tumor detection using segmentation and threshold operations. Int. J. Comput. Sci. Inf. Technol. 5(1), 607–611 (2014)
Arya, A., Bhateja, V., Nigam, M., Bhadauria, A.S.: Enhancement of brain MRT1/T2 images using mathematical morphology. In: Proceedings of 3rd International Conference on ICT, vol. 933, pp. 833–840. Springer Singapore (2019)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, pp. 689–794. Pearson Education, Chap. 10 (2009)
The Whole Brain Atlas, https://www.med.harvard.edu/aanlib/home.html
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Bhateja, V., Nigam, M., Bhadauria, A.S. (2021). Region Labeling Based Brain Tumor Segmentation from MR Images. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-0878-0_81
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DOI: https://doi.org/10.1007/978-981-16-0878-0_81
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