Morphometric characteristics in discrete domain for brain tumor recognition
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)...
- Autores:
-
Silva, Jesús
Zilberman, Jack
Bravo Núñez, Narledis
Varela Izquierdo, Noel
Pineda, Omar
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7292
- Acceso en línea:
- https://hdl.handle.net/11323/7292
https://repositorio.cuc.edu.co/
- Palabra clave:
- Brain tumor
Degree of malignancy
Morphometric characteristics
Recognition
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Morphometric characteristics in discrete domain for brain tumor recognition |
| title |
Morphometric characteristics in discrete domain for brain tumor recognition |
| spellingShingle |
Morphometric characteristics in discrete domain for brain tumor recognition Brain tumor Degree of malignancy Morphometric characteristics Recognition |
| title_short |
Morphometric characteristics in discrete domain for brain tumor recognition |
| title_full |
Morphometric characteristics in discrete domain for brain tumor recognition |
| title_fullStr |
Morphometric characteristics in discrete domain for brain tumor recognition |
| title_full_unstemmed |
Morphometric characteristics in discrete domain for brain tumor recognition |
| title_sort |
Morphometric characteristics in discrete domain for brain tumor recognition |
| dc.creator.fl_str_mv |
Silva, Jesús Zilberman, Jack Bravo Núñez, Narledis Varela Izquierdo, Noel Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Zilberman, Jack Bravo Núñez, Narledis Varela Izquierdo, Noel Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Brain tumor Degree of malignancy Morphometric characteristics Recognition |
| topic |
Brain tumor Degree of malignancy Morphometric characteristics Recognition |
| 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. |
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2020 |
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2020-11-12T21:10:53Z |
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2020-11-12T21:10:53Z |
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2020 |
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2021-06-19 |
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Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Corporación Universidad de la Costa |
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eng |
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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) 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 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 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) 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) 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) 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) 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 Moitra, D., Mandal, R.: Review of brain tumor detection using pattern recognition techniques. Int. J. Comput. Sci. Eng. 5(2), 121–123 (2017) 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) 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) Chen, S., Ding, C., Liu, M.: Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn. 88, 90–100 (2019) 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/ 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) 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 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 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) |
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Silva, JesúsZilberman, JackBravo Núñez, NarledisVarela Izquierdo, NoelPineda, Omar2020-11-12T21:10:53Z2020-11-12T21:10:53Z20202021-06-192194-5357https://hdl.handle.net/11323/7292Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Silva, JesúsZilberman, Jack-will be generated-orcid-0000-0003-0956-4059-600Bravo Núñez, NarledisVarela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85089719105&doi=10.1007%2f978-3-030-53036-5_9&partnerID=40&md5=929a44084e2e0a2112ec8c63c31239a9Brain tumorDegree of malignancyMorphometric characteristicsRecognitionMorphometric characteristics in discrete domain for brain tumor recognitionPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionSaba, 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)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.A2269Gamero, 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 2018Özyurt, 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)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)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)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)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 2013Moitra, D., Mandal, R.: Review of brain tumor detection using pattern recognition techniques. Int. J. Comput. Sci. Eng. 5(2), 121–123 (2017)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)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)Chen, S., Ding, C., Liu, M.: Dual-force convolutional neural networks for accurate brain tumor segmentation. Pattern Recogn. 88, 90–100 (2019)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/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)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-yViloria, 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 2018Thivya 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. 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