Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography

Pathological ovary is categorized into cystic tumors, solid tumors and mixed, according to the content of the affected ovary. Accordingly, the degree of benignity or malignity thereof is established. The imaging study for the preliminary morphological assessment of PO is ultrasound, in its pelvic an...

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Autores:
Valbuena, O
Vera, M
Vera, M I
Gelvez-Almeida, E
Huérfano, Y
Borrero, M
Salazar-Torres, J
Salazar, W
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/5113
Acceso en línea:
https://hdl.handle.net/20.500.12442/5113
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.title.eng.fl_str_mv Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
spellingShingle Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title_short Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title_full Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title_fullStr Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title_full_unstemmed Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
title_sort Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomography
dc.creator.fl_str_mv Valbuena, O
Vera, M
Vera, M I
Gelvez-Almeida, E
Huérfano, Y
Borrero, M
Salazar-Torres, J
Salazar, W
dc.contributor.author.none.fl_str_mv Valbuena, O
Vera, M
Vera, M I
Gelvez-Almeida, E
Huérfano, Y
Borrero, M
Salazar-Torres, J
Salazar, W
description Pathological ovary is categorized into cystic tumors, solid tumors and mixed, according to the content of the affected ovary. Accordingly, the degree of benignity or malignity thereof is established. The imaging study for the preliminary morphological assessment of PO is ultrasound, in its pelvic and transvaginal modalities, for which wellestablished criteria are available. Once the ultrasound findings suggest malignancy, complementary studies such as abdominal-pelvic tomography images and tumor markers are requested. This type of images has challenging problems called noise, artifacts and low contrast. In this paper, in order to address these problems, a computational technique is proposed to characterize a pathological ovary. To do this, a thresholding and the median and gradient magnitude filters are applied, preliminarily, to complete the preprocessing stage. Then, during the segmentation, the algorithm of region growing is used to extract the threedimensional morphology of the pathological ovary. Using this morphology, the volume of the pathological ovary is calculated and it allows selecting the surgical-medical behavior to approach this kind of ovary. The validation of the proposed technique indicates that the results are promising. This technique can be useful in the detection and monitoring the diseases linked to pathological ovary.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-04-15T04:56:40Z
dc.date.available.none.fl_str_mv 2020-04-15T04:56:40Z
dc.type.eng.fl_str_mv article
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dc.type.driver.eng.fl_str_mv article
dc.identifier.issn.none.fl_str_mv 17426596
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/5113
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/5113
dc.language.iso.eng.fl_str_mv eng
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dc.format.mimetype.spa.fl_str_mv pdf
dc.publisher.eng.fl_str_mv IOP Publishing
dc.source.eng.fl_str_mv Journal of Physics: Conference Series
Vol. 1403 (2019)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012020
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spelling Valbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9Vera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eVera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eGelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deHuérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fBorrero, Mfe2c6598-d41a-4440-a179-f32b49a37c90Salazar-Torres, J40a2a6c9-3e39-4994-9b5a-1c6112bd8000Salazar, Wf373f4f6-6308-4037-aa3f-bbcbde9cbe1b2020-04-15T04:56:40Z2020-04-15T04:56:40Z201917426596https://hdl.handle.net/20.500.12442/5113Pathological ovary is categorized into cystic tumors, solid tumors and mixed, according to the content of the affected ovary. Accordingly, the degree of benignity or malignity thereof is established. The imaging study for the preliminary morphological assessment of PO is ultrasound, in its pelvic and transvaginal modalities, for which wellestablished criteria are available. Once the ultrasound findings suggest malignancy, complementary studies such as abdominal-pelvic tomography images and tumor markers are requested. This type of images has challenging problems called noise, artifacts and low contrast. In this paper, in order to address these problems, a computational technique is proposed to characterize a pathological ovary. To do this, a thresholding and the median and gradient magnitude filters are applied, preliminarily, to complete the preprocessing stage. Then, during the segmentation, the algorithm of region growing is used to extract the threedimensional morphology of the pathological ovary. Using this morphology, the volume of the pathological ovary is calculated and it allows selecting the surgical-medical behavior to approach this kind of ovary. The validation of the proposed technique indicates that the results are promising. This technique can be useful in the detection and monitoring the diseases linked to pathological ovary.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1403 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1414/1/012020Volumetric quantification in ovarian pathology using abdomino-pelvic computed tomographyarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Blaustein A 1977 Pathology of the female genital tract ed A Blaustein (New York: Springer) Anatomy and histology of the human ovary 438 Chapter 15Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams S T, Ali Hassan M, Bowtell D D L, Gabra H, Fotopoulou C, Rockall A and Aboagye E O 2019 A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic and molecular-phenotypes of epithelial ovarian cancer Nature Communications 10 764Cigale B and Zazula D 2004 Segmentation of ovarian ultrasound images using cellular neural networks International Journal of Pattern Recognition and Artificial Intelligence 18 563Ramya M and Kiruthika V 2014 Fifth International Conference on Signal and Image Processing (Bangalore: IEEE) Automatic segmentation of ovarian follicle using k-meansSonigo C, Jankowski S, Yoo O, Trassard O, Bousquet N, Grynberg M, Beau I and Binart N 2018 High-throughput ovarian follicle counting by an innovative deep learning approach Scientific Reports 8 13499Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)González R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall)Petrou M and Bosdogianni P 2003 Image processing the fundamentals (New York: John Wiley & Sons Inc)Huérfano Y, Vera M, Mar A and Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation Journal of Physics: Conference Series 1160 012003Burden R and Faires D 2010 Numerical analysis (Mexico: Cengage Learning)Saénz F, Vera M, Huérfano Y, Molina V, Martinez L, Vera MI, Salazar W, Gelvez E, Salazar J, Valbuena O, Robles H, Bautista M and Arango J 2018 Brain Hematoma Computational Segmentation Journal of Physics: Conference Series 1126 012071Dice L 1945 Measures of the amount of ecologic association between species Ecology 26 29ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf562428https://bonga.unisimon.edu.co/bitstreams/1c21a873-5a66-4f1b-b9ec-805ad6f405a2/download868a73417879e6bc439106b74d21e1c2MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/1fdbdf1e-29c3-4a78-a3ed-a11e9544170f/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/e4f8523c-2cae-4bd8-bcbc-c496f5c998f8/download733bec43a0bf5ade4d97db708e29b185MD53TEXTVolumetric_quantification_ovarian_pathology.pdf.txtVolumetric_quantification_ovarian_pathology.pdf.txtExtracted texttext/plain15970https://bonga.unisimon.edu.co/bitstreams/4bb20eb9-ec29-4e80-8df7-d1167f37634c/download58489329fd834d48869b4c3f98dd2ef9MD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain16508https://bonga.unisimon.edu.co/bitstreams/2f80f49d-5fec-4f46-872c-6d1b2ae8e00b/download6f627a8f9add621b664de4c227b4f1a1MD56THUMBNAILVolumetric_quantification_ovarian_pathology.pdf.jpgVolumetric_quantification_ovarian_pathology.pdf.jpgGenerated Thumbnailimage/jpeg1293https://bonga.unisimon.edu.co/bitstreams/eb92d031-a818-4a17-a45e-76e7f31765ec/download387b9adecc62bceb6efa0465827f7cb8MD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3343https://bonga.unisimon.edu.co/bitstreams/f0ca0223-3aa8-4052-b8a3-c1b7d64a902b/download161a42e8dcc2906bf8a807057b6cf837MD5720.500.12442/5113oai:bonga.unisimon.edu.co:20.500.12442/51132024-08-14 21:53:04.829http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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