Texture analysis in skull magnetic resonance imaging

raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15–45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The...

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Autores:
amelec, viloria
de la Hoz, Ethel
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:11323/7277
Acceso en línea:
https://hdl.handle.net/11323/7277
https://repositorio.cuc.edu.co/
Palabra clave:
Genetic algorithm
Skull magnetic resonance imaging
Texture analysis
Rights
closedAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_d52a12cc511949000615564211e9a7dd
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7277
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Texture analysis in skull magnetic resonance imaging
title Texture analysis in skull magnetic resonance imaging
spellingShingle Texture analysis in skull magnetic resonance imaging
Genetic algorithm
Skull magnetic resonance imaging
Texture analysis
title_short Texture analysis in skull magnetic resonance imaging
title_full Texture analysis in skull magnetic resonance imaging
title_fullStr Texture analysis in skull magnetic resonance imaging
title_full_unstemmed Texture analysis in skull magnetic resonance imaging
title_sort Texture analysis in skull magnetic resonance imaging
dc.creator.fl_str_mv amelec, viloria
de la Hoz, Ethel
Pineda, Omar
dc.contributor.author.spa.fl_str_mv amelec, viloria
de la Hoz, Ethel
Pineda, Omar
dc.subject.spa.fl_str_mv Genetic algorithm
Skull magnetic resonance imaging
Texture analysis
topic Genetic algorithm
Skull magnetic resonance imaging
Texture analysis
description raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15–45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The reconstruction of bone structures after physical damage generated by such an unfortunate event as disease or trauma can range from the implementation of prostheses to the engineering of artificial bone implants [2]. To make a virtual or physical model of any human anatomy, it must first be captured in three dimensions in a way that can be used by computational processes. Most hospital scanners capture data from the entire body both internally and externally. These machines are typically medical imaging devices capable of scanning the entire human body, among which, the most common is the magnetic resonance imaging (MRI) equipment [3]. The goal of the research is to analyze the texture in magnetic resonance imaging and its relationship to bone mineral content (BMC) using simple linear regression.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-12T17:33:59Z
dc.date.available.none.fl_str_mv 2020-11-12T17:33:59Z
dc.date.issued.none.fl_str_mv 2020
dc.date.embargoEnd.none.fl_str_mv 2021-05-07
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7277
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2194-5357
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7277
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Galm, Brandon P., Buckless, C., Swearingen, B., Torriani, M., Klibanski, A., Bredella, Miriam A., Tritos, Nicholas A.: MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands. Pituitary 23(3), 212–222 (2020)
Mitra, A., Tripathi, P. C., Bag, S.: Identification of astrocytoma grade using intensity, texture, and shape based features. In: Soft Computing for Problem Solving, pp. 455–465. Springer, Singapore (2020)
Sabino, M.: Techniques for the manufacture of polymeric scaffolding with applications in fabric engineering. Rev. Latin Am. Metal, 120–146 (2017)
Felsenberg, D.: Structure and function of bone: supporting structure of collagen and hydroxyapatite. Pharm. Our Time 30(6), 488–494 (2001)
Bahadure, N.B., Ray, A.K., Thethi, H.P.: Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imag. (2017)
Latha, M., Kavitha, G.: Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. Magn. Reson. Mater. Phys. Biol. Med. 31(4), 483–499 (2018)
Avizenna, M.H., Soesanti, I., Ardiyanto, I.: Classification of brain magnetic resonance images based on statistical texture. In: 2018 1st International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering-Bioinformatics and Biomedical Engineering, vol. 1, pp. 1–5. IEEE (2018)
Ali, A.H., Al-hadi, S.A., Naeemah, M.R., Mazher, A.N.: Classification of brain lesion using K-nearest neighbor technique and texture analysis. In: Journal Physics: Conference Series, vol. 1178, no. (1), p. 012018. IOP Publishing (2019)
Somwanshi, D.K., Yadav, A.K., Roy, R.: Medical images texture analysis: a review. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp. 436–441. IEEE (2017)
Paul, T.U., Ghosh, A., Bandhyopadhyay, S.K.: Brain tumor texture analysis-using wavelets and fractals. Int. J. Med. Imag. 4(4), 23 (2016)
Kawashima, Y., Fujita, A., Buch, K., Li, B., Qureshi, M.M., Chapman, M.N., Sakai, O.: Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. Eur. J. Radiol. 116, 212–218 (2019)
Ta, D., Khan, M., Ishaque, A., Seres, P., Eurich, D., Yang, Y.H., Kalra, S.: Reliability of 3D texture analysis: a multicenter MRI study of the brain. J. Magn. Reson. Imag. 51(4), 1200–1209 (2019)
Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)
Chondro, P., Hu, H.C., Hung, H.Y., Chang, S.Y., Li, L.P.H., Ruan, S.J.: An effective occipitomental view enhancement based on adaptive morphological texture analysis. IEEE J. Biomed. Health Inform. 21(4), 1105–1113 (2016)
Nair, J.K.R., Vallières, M., Mascarella, M.A., El Sabbagh, N., Duchatellier, C.F., Zeitouni, A., Shenouda, G., Chankowsky, J.: Magnetic resonance imaging texture analysis predicts recurrence in patients with nasopharyngeal carcinoma. Can. Assoc. Radiol. J. 70(4), 394–402 (2019)
Li, Y., Li, M.M., Zhang, Y., Cheng, J.L., Shang, Z.G., Bu, C.X.: Utility of texture analysis of magnetic resonance imaging in differential diagnosis of common pediatric cerebellar tumors in children. Zhonghua yi xue za zhi 96(23), 1853–1855 (2016)
Lee, K.M., Kim, H.G., Lee, Y.H., Kim, E.J.: mDixon-based texture analysis of an intraosseous lipoma: a case report and current review for the dental clinician. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 125(3), e67–e71 (2018)
Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)
Chang, H.H., Hsieh, C.C.: Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3421–3424. IEEE 2017
Lee, S., Lee, H., Kim, K.W.: Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume. J. Psychiatry Neurosci.: JPN 45(1), 7 (2020)
Ke, C., Chen, H., Lv, X., Li, H., Zhang, Y., Chen, M., Hu, D., Ruan, G., Zhang, Y., Zhang, Y., Liu, L.: Differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MRI. J. Magn. Reson. Imag. 51(6), 1810–1820 (2019)
Iqbal, S., Khan, M.U.G., Saba, T., Rehman, A.: Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed. Eng. Lett. 8(1), 5–28 (2018)
Kim, D., Wang, N.C., Ravikumar, V., Raghuram, D.R., Li, J., Patel, A., Wendt, R.E., Rao, G., Rao, A.: Prediction of 1p/19q codeletion in diffuse glioma patients using preoperative multiparametric magnetic resonance imaging. Front. Comput. Neurosci. 13, 52 (2019)
Basheera, S., Ram, M.S.S.: Convolution neural network-based Alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer’s & Dementia: Transl. Res. Clin. Interv. 5, 974–986 (2019)
GGrist, J.T., Withey, S., MacPherson, L., Oates, A., Powell, S., Novak, J., Abernethy, L., Pizer, B., Grundy, R., Bailey, S., Mitra, D., Arvanitis, T.N., Auer, D.P., Avula, S., Peet, A.C.: Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study. NeuroImage: Clin. 25, 102172 (2020)
Baiocco, S., Sah, B.R., Mallia, A., Kelly-Morland, C., Neji, R., Stirling, J.J., Jeljeli, S., Bevilacqua, A., Cook, G.J., Goh, V.: Exploratory radiomic features from integrated 18 F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer. Eur. J. Nucl. Med. Mol. Imag. 46(7), 1478–1484 (2019)
Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to postgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
Su, C.Q., Zhang, X., Pan, T., Chen, X.T., Chen, W., Duan, S.F., Ji, J., Hu, W.X., Lu, S.S., Hong, X.N.: Texture analysis of high b-value diffusion-weighted imaging for evaluating consistency of pituitary macroadenomas. J. Magn. Reson. Imag. 51(5), 1507–1513 (2019)
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spelling amelec, viloriade la Hoz, EthelPineda, Omar2020-11-12T17:33:59Z2020-11-12T17:33:59Z20202021-05-072194-5357https://hdl.handle.net/11323/7277Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/raumatic brain injury (TBI) represents a serious public health problem worldwide. It is the most common cause of death and disability in the young population (aged 15–45 years), with major family, social and economic implications [1]. In medical terms, the human body can be studied as an object. The reconstruction of bone structures after physical damage generated by such an unfortunate event as disease or trauma can range from the implementation of prostheses to the engineering of artificial bone implants [2]. To make a virtual or physical model of any human anatomy, it must first be captured in three dimensions in a way that can be used by computational processes. Most hospital scanners capture data from the entire body both internally and externally. These machines are typically medical imaging devices capable of scanning the entire human body, among which, the most common is the magnetic resonance imaging (MRI) equipment [3]. The goal of the research is to analyze the texture in magnetic resonance imaging and its relationship to bone mineral content (BMC) using simple linear regression.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600de la Hoz, EthelPineda, 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/record/display.uri?eid=2-s2.0-85089209036&doi=10.1007%2f978-3-030-51859-2_12&origin=inward&txGid=1e875e0c53741f62be937f585a57c8f2Genetic algorithmSkull magnetic resonance imagingTexture analysisTexture analysis in skull magnetic resonance imagingPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionGalm, Brandon P., Buckless, C., Swearingen, B., Torriani, M., Klibanski, A., Bredella, Miriam A., Tritos, Nicholas A.: MRI texture analysis in acromegaly and its role in predicting response to somatostatin receptor ligands. Pituitary 23(3), 212–222 (2020)Mitra, A., Tripathi, P. C., Bag, S.: Identification of astrocytoma grade using intensity, texture, and shape based features. In: Soft Computing for Problem Solving, pp. 455–465. Springer, Singapore (2020)Sabino, M.: Techniques for the manufacture of polymeric scaffolding with applications in fabric engineering. Rev. Latin Am. Metal, 120–146 (2017)Felsenberg, D.: Structure and function of bone: supporting structure of collagen and hydroxyapatite. Pharm. Our Time 30(6), 488–494 (2001)Bahadure, N.B., Ray, A.K., Thethi, H.P.: Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imag. (2017)Latha, M., Kavitha, G.: Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. Magn. Reson. Mater. Phys. Biol. Med. 31(4), 483–499 (2018)Avizenna, M.H., Soesanti, I., Ardiyanto, I.: Classification of brain magnetic resonance images based on statistical texture. In: 2018 1st International Conference on Bioinformatics, Biotechnology, and Biomedical Engineering-Bioinformatics and Biomedical Engineering, vol. 1, pp. 1–5. IEEE (2018)Ali, A.H., Al-hadi, S.A., Naeemah, M.R., Mazher, A.N.: Classification of brain lesion using K-nearest neighbor technique and texture analysis. In: Journal Physics: Conference Series, vol. 1178, no. (1), p. 012018. IOP Publishing (2019)Somwanshi, D.K., Yadav, A.K., Roy, R.: Medical images texture analysis: a review. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp. 436–441. IEEE (2017)Paul, T.U., Ghosh, A., Bandhyopadhyay, S.K.: Brain tumor texture analysis-using wavelets and fractals. Int. J. Med. Imag. 4(4), 23 (2016)Kawashima, Y., Fujita, A., Buch, K., Li, B., Qureshi, M.M., Chapman, M.N., Sakai, O.: Using texture analysis of head CT images to differentiate osteoporosis from normal bone density. Eur. J. Radiol. 116, 212–218 (2019)Ta, D., Khan, M., Ishaque, A., Seres, P., Eurich, D., Yang, Y.H., Kalra, S.: Reliability of 3D texture analysis: a multicenter MRI study of the brain. J. Magn. Reson. Imag. 51(4), 1200–1209 (2019)Varela, N., Silva, J., Gonzalez, F.M., Palencia, P., Palma, H.H., Pineda, O.B.: Method for the recovery of images in databases of rice grains from visual content. Procedia Comput. Sci. 170, 983–988 (2020)Chondro, P., Hu, H.C., Hung, H.Y., Chang, S.Y., Li, L.P.H., Ruan, S.J.: An effective occipitomental view enhancement based on adaptive morphological texture analysis. IEEE J. Biomed. Health Inform. 21(4), 1105–1113 (2016)Nair, J.K.R., Vallières, M., Mascarella, M.A., El Sabbagh, N., Duchatellier, C.F., Zeitouni, A., Shenouda, G., Chankowsky, J.: Magnetic resonance imaging texture analysis predicts recurrence in patients with nasopharyngeal carcinoma. Can. Assoc. Radiol. J. 70(4), 394–402 (2019)Li, Y., Li, M.M., Zhang, Y., Cheng, J.L., Shang, Z.G., Bu, C.X.: Utility of texture analysis of magnetic resonance imaging in differential diagnosis of common pediatric cerebellar tumors in children. Zhonghua yi xue za zhi 96(23), 1853–1855 (2016)Lee, K.M., Kim, H.G., Lee, Y.H., Kim, E.J.: mDixon-based texture analysis of an intraosseous lipoma: a case report and current review for the dental clinician. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 125(3), e67–e71 (2018)Viloria, A., Lezama, O.B.P.: Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput. Sci. 151, 1201–1206 (2019)Chang, H.H., Hsieh, C.C.: Brain segmentation in MR images using a texture-based classifier associated with mathematical morphology. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3421–3424. IEEE 2017Lee, S., Lee, H., Kim, K.W.: Magnetic resonance imaging texture predicts progression to dementia due to Alzheimer disease earlier than hippocampal volume. J. Psychiatry Neurosci.: JPN 45(1), 7 (2020)Ke, C., Chen, H., Lv, X., Li, H., Zhang, Y., Chen, M., Hu, D., Ruan, G., Zhang, Y., Zhang, Y., Liu, L.: Differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MRI. J. Magn. Reson. Imag. 51(6), 1810–1820 (2019)Iqbal, S., Khan, M.U.G., Saba, T., Rehman, A.: Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed. Eng. Lett. 8(1), 5–28 (2018)Kim, D., Wang, N.C., Ravikumar, V., Raghuram, D.R., Li, J., Patel, A., Wendt, R.E., Rao, G., Rao, A.: Prediction of 1p/19q codeletion in diffuse glioma patients using preoperative multiparametric magnetic resonance imaging. Front. Comput. Neurosci. 13, 52 (2019)Basheera, S., Ram, M.S.S.: Convolution neural network-based Alzheimer’s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer’s & Dementia: Transl. Res. Clin. Interv. 5, 974–986 (2019)GGrist, J.T., Withey, S., MacPherson, L., Oates, A., Powell, S., Novak, J., Abernethy, L., Pizer, B., Grundy, R., Bailey, S., Mitra, D., Arvanitis, T.N., Auer, D.P., Avula, S., Peet, A.C.: Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: a multi-site study. NeuroImage: Clin. 25, 102172 (2020)Baiocco, S., Sah, B.R., Mallia, A., Kelly-Morland, C., Neji, R., Stirling, J.J., Jeljeli, S., Bevilacqua, A., Cook, G.J., Goh, V.: Exploratory radiomic features from integrated 18 F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer. Eur. J. Nucl. Med. Mol. Imag. 46(7), 1478–1484 (2019)Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to postgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)Su, C.Q., Zhang, X., Pan, T., Chen, X.T., Chen, W., Duan, S.F., Ji, J., Hu, W.X., Lu, S.S., Hong, X.N.: Texture analysis of high b-value diffusion-weighted imaging for evaluating consistency of pituitary macroadenomas. J. Magn. Reson. 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