dc.creator | Silva, Jesús | |
dc.creator | Zilberman, Jack | |
dc.creator | Bravo Núñez, Narledis | |
dc.creator | Varela Izquierdo, Noel | |
dc.creator | Pineda, Omar | |
dc.date | 2020-11-12T21:10:53Z | |
dc.date | 2020-11-12T21:10:53Z | |
dc.date | 2020 | |
dc.date | 2021-06-19 | |
dc.date.accessioned | 2023-10-03T19:48:43Z | |
dc.date.available | 2023-10-03T19:48:43Z | |
dc.identifier | 2194-5357 | |
dc.identifier | https://hdl.handle.net/11323/7292 | |
dc.identifier | Corporación Universidad de la Costa | |
dc.identifier | REDICUC - Repositorio CUC | |
dc.identifier | https://repositorio.cuc.edu.co/ | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9172265 | |
dc.description | World Health Organization (WHO) classifies brain tumors by their level of aggressiveness into four grades depending on their aggressiveness or malignancy as I to IV respectively [1]. From this classification of primary brain tumors, the four categories can be considered in two groups: Low Grade (LG) and High Grade (HG), in which the LG group is composed of grade I and II brain tumors, while the HG group is composed of grades III and IV brain tumors [2]. This paper focuses on the morphometric analysis of brain tumors and the study of the correlation of tumor shape with its degree of malignancy. | |
dc.format | application/pdf | |
dc.format | application/pdf | |
dc.language | eng | |
dc.publisher | Corporación Universidad de la Costa | |
dc.relation | Saba, T., Mohamed, A.S., El-Affendi, M., Amin, J., Sharif, M.: Brain tumor detection using fusion of hand crafted and deep learning features. Cogn. Syst. Res. 59, 221–230 (2020) | |
dc.relation | Blanchet, L., Krooshof, P., Postma, G., Idema, A., Goraj, B., Heerschap, A., Buydens, L.: Discrimination between metastasis and glioblastoma multiforme based on morphometric analysis of MR images. Am. J. Neuroradiol. 32(1), 67–73 (2011). http://www.ajnr.org/content/early/2010/11/04/ajnr.A2269 | |
dc.relation | Gamero, W.M., Agudelo-Castañeda, D., Ramirez, M.C., Hernandez, M.M., Mendoza, H.P., Parody, A., Viloria, A.: Hospital admission and risk assessment associated to exposure of fungal bioaerosols at a municipal landfill using statistical models. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 210–218. Springer, Cham, November 2018 | |
dc.relation | Özyurt, F., Sert, E., Avcı, D.: An expert system for brain tumor detection: fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med. Hypotheses 134, 109433 (2020) | |
dc.relation | Wu, Q., Wu, L., Wang, Y., Zhu, Z., Song, Y., Tan, Y., Wang, X.F., Li, J., Kang, D., Yang, C.J.: Evolution of DNA aptamers for malignant brain tumor gliosarcoma cell recognition and clinical tissue imaging. Biosens. Bioelectron. 80, 1–8 (2016) | |
dc.relation | Kharrat, A., Mahmoud, N.E.J.I.: Feature selection based on hybrid optimization for magnetic resonance imaging brain tumor classification and segmentation. Appl. Med. Inf. 41(1), 9–23 (2019) | |
dc.relation | Sharif, M., Amin, J., Raza, M., Yasmin, M., Satapathy, S.C.: An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recogn. Lett. 129, 150–157 (2020) | |
dc.relation | Chang, H., Borowsky, A., Spellman, P., Parvin, B.: Classification of tumor histology via morphometric context. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2203–2210, June 2013 | |
dc.relation | Moitra, D., Mandal, R.: Review of brain tumor detection using pattern recognition techniques. Int. J. Comput. Sci. Eng. 5(2), 121–123 (2017) | |
dc.relation | Einenkel, J., Braumann, U.D., Horn, L.C., Pannicke, N., Kuska, J.P., Schhütz, A., Hentschel, B., Hockel, M.: Evaluation of the invasion front pattern of squamous cell cervical carcinoma by measuring classical and discrete compactness. Comput. Med. Imaging Graph 31, 428–435 (2007) | |
dc.relation | Gomathi, P., Baskar, S., Shakeel, M.P., Dhulipala, S.V.: Numerical function optimization in brain tumor regions using reconfigured multi-objective bat optimization algorithm. J. Med. Imaging Health Inf. 9(3), 482–489 (2019) | |
dc.relation | Chen, S., Ding, C., Liu, M.: Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn. 88, 90–100 (2019) | |
dc.relation | Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013). http://www.jmir.org/2013/11/e245/ | |
dc.relation | Amin, J., Sharif, M., Gul, N., Yasmin, M., Shad, S.A.: Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recogn. Lett. 129, 115–122 (2020) | |
dc.relation | Kim, B., Tabori, U., Hawkins, C.: An update on the CNS manifestations of brain tumor polyposis syndromes. Acta Neuropathol. 139, 703–715 (2020). https://ezproxy.cuc.edu.co:2067/10.1007/s00401-020-02124-y | |
dc.relation | Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J.P., Parody, A., Bent, D.E.S., López, L.A.B.: Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In: International Conference on Data Mining and Big Data, pp. 304–313. Springer, Cham, June 2018 | |
dc.relation | Thivya Roopini, I., Vasanthi, M., Rajinikanth, V., Rekha, M., Sangeetha, M.: Segmentation of tumor from brain MRI using fuzzy entropy and distance regularised level set. In: Nandi, A.K., Sujatha, N., Menaka, R., Alex, J.S.R. (eds.) Computational Signal Processing and Analysis, pp. 297–304. Springer, Singapore (2018) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.rights | info:eu-repo/semantics/closedAccess | |
dc.rights | http://purl.org/coar/access_right/c_14cb | |
dc.source | Advances in Intelligent Systems and Computing | |
dc.source | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089719105&doi=10.1007%2f978-3-030-53036-5_9&partnerID=40&md5=929a44084e2e0a2112ec8c63c31239a9 | |
dc.subject | Brain tumor | |
dc.subject | Degree of malignancy | |
dc.subject | Morphometric characteristics | |
dc.subject | Recognition | |
dc.title | Morphometric characteristics in discrete domain for brain tumor recognition | |
dc.type | Pre-Publicación | |
dc.type | http://purl.org/coar/resource_type/c_816b | |
dc.type | Text | |
dc.type | info:eu-repo/semantics/preprint | |
dc.type | info:eu-repo/semantics/draft | |
dc.type | http://purl.org/redcol/resource_type/ARTOTR | |
dc.type | info:eu-repo/semantics/acceptedVersion | |
dc.type | http://purl.org/coar/version/c_ab4af688f83e57aa | |