Smart operator for the human liver automatic segmentation, present in medical images
| dc.contributor.author | Vera, M | |
| dc.contributor.author | Sáenz, F | |
| dc.contributor.author | Huérfano, Y | |
| dc.contributor.author | Gelvez-Almeida, E | |
| dc.contributor.author | Vera, M I | |
| dc.contributor.author | Salazar-Torres, J | |
| dc.contributor.author | Valbuena, O | |
| dc.date.accessioned | 2020-04-15T03:21:31Z | |
| dc.date.available | 2020-04-15T03:21:31Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | The segmentation of the human body organ called liver is a highly challenging problem due to the noise, artifacts and the low contrast exhibited by the anatomical structures located around the liver and that are present in digital images, generated by any modality of medical images. The main modalities are: ultrasound, nuclear emission, magnetic resonance and the gold standard called multi-slice computed tomography. In this paper, with the objective of to address this problem, we consider multi-slice computed tomography images and we propose an automatic strategy based on two phases. In the first phase, a digital filtering bank is used for diminishing the noise effect and the artifacts impact in the quality of images. In the second phase, called liver detection, we use a smart operator based on least squares support vector machines for generating both the morphology and the volume of liver. The application of this strategy allows generating the morphology of the liver in a precise and efficient manner as it was demonstrated by the metrics used to assess its performance. These results are very important in clinical-surgical processes where both the shape and volume of liver are vital for monitoring some liver diseases that can affect the normal liver physiology. | eng |
| dc.format.mimetype | eng | |
| dc.identifier.issn | 17426596 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/5109 | |
| dc.language.iso | eng | eng |
| dc.publisher | IOP Publishing | eng |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.source | Journal of Physics: Conference Series | eng |
| dc.source | Vol. 1386 (2019) | eng |
| dc.source.uri | https://iopscience.iop.org/article/10.1088/1742-6596/1386/1/012132 | eng |
| dc.title | Smart operator for the human liver automatic segmentation, present in medical images | eng |
| dc.type | article | eng |
| dc.type.driver | article | eng |
| dcterms.references | Latarjet M and Ruiz A 2004 Anatomía Humana (Barcelona: Médica Panamericana) | spa |
| dcterms.references | Emiroglu R, Coskun M, Yilmaz U, Sevmis S, Ozcay F and Haberal M 2006 Safety of multidetector computed tomography in calculating liver volume for living-donor liver transplantation Transplantation Proc. 38 3576 | eng |
| dcterms.references | Meinzer H and Thorn M 2002 Computerized planning of liver surgery: an overview Computers & Graphics 26 569 | eng |
| dcterms.references | Lu R and Marziliano P 2005 Liver tumor volume estimation by semi-automatic segmentation method Proc. 27th Annual Conference (Shanghai: IEEE Engineering in Medicine and Biology Soc.) 1 3297 | eng |
| dcterms.references | Kim E, Oh J, Chun H, Choi B and Lee H 2018 Usefulness of fusion images of unenhanced and contrastenhanced arterial phase conebeam ct in the detection of viable hepatocellular carcinoma during transarterial chemoembolization Diagn. Interv. Radiol. 24 262 | eng |
| dcterms.references | Muthuswamy J and Kanmani B 2018 Optimization based liver contour extraction of abdominal ct images Int. J. Elec. & Comp. Eng. 8(6) 5061 | eng |
| dcterms.references | Tacher V, MingDe L, Chao M, Gjesteby L, Bhagat N, Mahammedi A, Ardon R, Mory B and Geschwind J 2013 Semi-automatic volumetric tumor segmentation for hepatocellular carcinoma: comparison between c-arm cone beam computed tomography and mri Acad. Radiol. 20(4) 446 | eng |
| dcterms.references | González R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall) | eng |
| dcterms.references | Huérfano Y, Vera M, Gelvez E, Salazar J, Del Mar A, Valbuena O and Molina V 2019 A computational strategy for the identification of pulmonary squamous cell carcinoma in computerized tomography images J. Phys.: Conf. Ser. 1160 012004 | eng |
| dcterms.references | Burden R and Faires D 2010 Numerical analysis (Ciudad de Mexico: Cengage learning) | eng |
| dcterms.references | Vera M, Medina R, Del Mar A, Arellano J, Huérfano Y and Bravo A 2019 An automatic technique for left ventricle segmentation from msct cardiac volumes J. Phys.: Conf. Ser. 1160 012001 | eng |
| dcterms.references | Dice L 1945 Measures of the amount of ecologic association between species Ecology 26(3) 29 | eng |
| oaire.version | info:eu-repo/semantics/publishedVersion | eng |
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