Brain tumor segmentation of normal and lesion tissues using hybrid clustering and hierarchical centroid shape descriptor

References

• Abdel E, Elmogy M, Al-Awadi R. 2015. Brain tumor segmentation based on a hybrid clustering technique. Egypt Inform J. 16(1):71–81.
   Web of Science ®Google Scholar
• Anitha V, Murugavalli S. 2016. Brain tumor classification using two-tier classifier with adaptive segmentation technique. IET Comput Vis. 10(1):9–17.
   Web of Science ®Google Scholar
• Aubert-Broche B, Evans AC, Collins L. 2006. A new improved version of the realistic digital brain phantom. NeuroImage. 32(1):138–145.
   PubMed Web of Science ®Google Scholar
• Bauer S, Fejes T, Slotboom J, Wiest R, Nolte LP, Reyes M. 2012. Segmentation of brain tumor images based on integrated hierarchical classification and regularization. MICCAI Challenge Multimodal Brain Tumor Segment. 10–13.
   Google Scholar
• Binaghi E, Pedoia V, Balbi S. 2018. Meningioma and peritumoral edema segmentation of preoperative MRI brain scans. Comput Methods Biomech Biomed Eng Imaging Visual. 6(4):362–370.
   Web of Science ®Google Scholar
• Demirhan A, Guler I. 2010. Image segmentation using self-organizing maps and gray level co-occurrence matrices. J Faculty Eng Architect Gazi Univ. 25(2):285291.
   Web of Science ®Google Scholar
• Demirhan A, Mustafa T, Inan G. 2015. Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform. 19(4):1451–1458.
   PubMed Web of Science ®Google Scholar
• Deng WQ, Li XM, Gao X, Zhang CM. 2016. A modified fuzzy c-mean algorithm for brain MR image segmentation and bias field correction. J Comput Sci Technol. 31(3):501–511.
   Web of Science ®Google Scholar
• Flores-Sintas A, Cadenas J, Martin F. 1999. Membership functions in the fuzzy C-means algorithm. Fuzzy Sets Systems. 101(1):49–58.
   Web of Science ®Google Scholar
• Geremia E, Menze BH, Ayache N. 2012. Spatial decision forests for glioma segmentation in multi-channel MR images. MICCAI Challenge Multimodal Brain Tumor Segment. 14–18.
   Google Scholar
• Govindaraj V, Murugan PR. 2014. A complete automated algorithm for segmentation of tissues and identification of tumor region in T1, T2, and FLAIR brain images using optimization and clustering techniques. Int J Imaging Syst Technol. 24(4):313–325.
   Web of Science ®Google Scholar
• Ilunga-Mbuyamba E, AvinaCervantes JG, GarciaPerez A, de Jesus RomeroTroncoso R, AguirreRamos H, CruzAceves I, Chalopin C. 2017. Localized active contour model with background intensity compensation applied on automatic MR brain tumor segmentation. Neurocomputing. 220:84–97.
   Web of Science ®Google Scholar
• Ilunga-Mbuyamba E, Avina-Cervantes JG, Lindner D, Guerrero-Turrubiates J, Chalopin C 2016. Automatic brain tumor tissue detection based on hierarchical centroid shape descriptor in Tl-weighted MR images. IEEE International Conference on Electronics Communications and Computers, Cholula, Mexico. 62–67.
   Google Scholar
• Jimnez-Alaniz JR, Medina-Bauelos V, Yez-Surez O. 2006. Data-driven brain MRI segmentation supported on edge confidence and a priori tissue information. IEEE Trans Med Imaging. 25(1):74–83.
   PubMed Web of Science ®Google Scholar
• Kaus M, Warfield SK, Jolesz FA, Kikinis R. 1999. Adaptive template moderated brain tumor segmentation in MRI. Bildverarbeitung fur die Medizin. 102–106.
   Google Scholar
• Kaya IE, Pehlivanl A, Sekizkarde EG, Ibrikci T. 2017. PCA based clustering for brain tumor segmentation of T1w MRI images. Comput Methods Programs Biomed. 140:19–28.
   PubMed Web of Science ®Google Scholar
• Loizou CP, Pantziaris M, Seimenis I, Pattichis CS 2009. Brain MR image normalization in texture analysis of multiple sclerosis. IEEE International Conference on Information Technology and Applications in Biomedicine, Larnaca, Cyprus. 1–5.
   Google Scholar
• Maksoud EA, Elmogy M. 2016. 3D brain tumor segmentation based on hybrid clustering techniques using multi-views of MRI. In: Dey N, Bhateja V, Hassanien A, editors. Springer medical imaging in clinical applications. Cham: Springer. p. 81–104.
   Google Scholar
• Maksoud EA, Elmogy M, Al-Awadi RM 2014. MRI brain tumor segmentation system based on hybrid clustering techniques. Springer International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt. 401–412.
   Google Scholar
• Menze BH, Geremia E, Ayache N, Szekely G. 2012. Segmenting glioma in multimodal images using a generative- discriminative model for brain lesion segmentation. MICCAI Challenge Multimodal Brain Tumor Segment. 49–55.
   Google Scholar
• Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, et al. 2015. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging. 34(10):1993–2024.
   PubMed Web of Science ®Google Scholar
• Nakamura K, Fisher E. 2009. Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients. Neuroimage. 44(3):769–776.
   PubMed Web of Science ®Google Scholar
• Niblack W. 1986. An introduction to digital image processing. Englewood Cliffs: Prentice-Hall.
   Google Scholar
• Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 9(1):62–66.
   Web of Science ®Google Scholar
• Pedoia V, Balbi S, Binaghi E 2015. Fully automatic brain tumor segmentation by using competitive EM and graph cut. In International Conference on Image Analysis and Processing. Springer International Conference on Image Analysis and Processing, Genoa, Italy. 568–578.
   Google Scholar
• Pham DL, Prince JL. 1999. An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recogn Lett. 20(1):57–68.
   Web of Science ®Google Scholar
• Popovic A, De la Fuente M, Engelhardt M, Radermacher K. 2007. Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Assist Radiol Surg. 2(3–4):169–181.
   Web of Science ®Google Scholar
• Ramudu K, Babu TR. 2018. Level set evolution of biomedical MRI and CT scan images using optimized fuzzy region clustering. Comput Methods Biomech Biomed Eng Imaging Visual. 1–2.
   PubMed Web of Science ®Google Scholar
• Sathish P, Elango NM. 2017. Exponential cuckoo search algorithm to radial basis neural network for automatic classification in MRI images. Comput Methods Biomech Biomed Eng Imaging Visual. 1–13.
   Web of Science ®Google Scholar
• Sauvola J, Pietikinen M. 2000. Adaptive document image binarization. Pattern Recogn. 33(2):225–236.
   Web of Science ®Google Scholar
• Shin HC. 2012. Hybrid clustering and logistic regression for multimodal brain tumor segmentation. MICCAI Challenge Multimodal Brain Tumor Segment. 32–35.
   Google Scholar
• Song Y, Ji Z, Sun Q, Zheng Y. 2016. A novel brain tumor segmentation from multi-modality MRI via a level-set-based model. J Signal Process Syst. 87(2):249–257.
   Web of Science ®Google Scholar
• Su B, Lu S, Tan CL Binarization of historical document images using the local maximum and minimum. 2010. Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, Boston, MA. 159–166.
   Google Scholar
• Taha AA, Hanbury A. 2015. Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 15(1):29.
   PubMed Web of Science ®Google Scholar
• Vishnuvarthanan A, Rajasekaran MP, Govindaraj V, Zhang Y, Thiyagarajan A. 2018. Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1-W, T2-W, FLAIR and MPR type magnetic resonance brain images. Expert Syst Appl. 95:280–311.
   Web of Science ®Google Scholar
• Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A. 2016. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput. 38:190–212.
   Web of Science ®Google Scholar
• Xiao Y, Hu J. 2012. Hierarchical random walker for multimodal brain tumor segmentation. MICCAI Challenge Multimodal Brain Tumor Segment. 36–40.
   Google Scholar
• Zikic D, Glocker B, Konukoglu E, Shotton J, Criminisi A, Ye D, Demiralp C, Thomas OM, Das T, Jena R, et al. 2012. Context-sensitive classification forests for segmentation of brain tumor tissues. MICCAI Challenge Multimodal Brain Tumor Segment. 1–9.
   Google Scholar