An automatic technique for left ventricle segmentation from msct cardiac volumes
| dc.contributor.author | Vera, M. | |
| dc.contributor.author | Medina, R. | |
| dc.contributor.author | Del Mar, A. | |
| dc.contributor.author | Arellano, J. | |
| dc.contributor.author | Huerfano, Y. | |
| dc.contributor.author | Bravo, A. | |
| dc.date.accessioned | 2019-03-06T20:03:13Z | |
| dc.date.available | 2019-03-06T20:03:13Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this research, an automatic technique to segment the left ventricle from the heart information in multislice computed tomography images is proposed. A preprocessing stage is considered as a necessary preliminary task for diminishing the artifacts impact in the image analysis. With this idea, a similarity enhancement that combines a smoothed version of the original volume with a processed volume using mathematical morphology is used. This preprocessing approach is compared with respect to other strategies. After, a volume-of-interest is defined in order to isolate the cavity using two cropping planes detected with least squares support vector machines. Finally, the segmentations are obtained using both a region growing algorithm and a level sets algorithm. The robustness of each enhancement strategy is validated by performing the segmentation of images. This evaluation considered the Dice score, and both volume and surface errors. A clinical dataset from 12 patients is used in the inter- and intra subject evaluation. During intra-subject validation the proposed scheme achieves the best results, while a modified version of this scheme achieved the best performance during inter-subject validation. | eng |
| dc.identifier.issn | 09767673 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12442/2736 | |
| dc.language.iso | eng | eng |
| dc.publisher | IOP Publishing | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
| dc.source | Journal of Physics | eng |
| dc.source | IOP Conf. Series: Journal of Physics: Conf. Series 1160 (2019) 012001 | eng |
| dc.source.uri | doi:10.1088/1742-6596/1160/1/012001 | eng |
| dc.subject | Heart | eng |
| dc.subject | Cardiovascular system | eng |
| dc.subject | Cardiography | eng |
| dc.title | An automatic technique for left ventricle segmentation from msct cardiac volumes | eng |
| dc.type | Conference | eng |
| dcterms.references | Faletra F, Pandian N and Ho S 2008 Anatomy of the heart by multislice computed tomography (UK: Wiley) | eng |
| dcterms.references | Kroft L, De Roos A and Geleijns J 2007 Artifacts in ECG–synchronized MDCT coronary angiography Amer. J. Roentgenol. 189 581 | eng |
| dcterms.references | Primak A, McCollough C, Bruesewitz M, Zhang J and Fletcher J 2006 Relationship between noise, dose, and pitch in cardiac multi–detector row CT Radiograph 26 1785 | eng |
| dcterms.references | Uzunbas M, Zhang S, Pohl K, Metaxas D and Axel L 2012 Segmentation of myocardium using deformable regions and graph cuts Proc. IEEE ISBI 254 | eng |
| dcterms.references | Zhou Y, Shi W, Chen W, Lin-Chen Y, Li Y, Tan L and Chen D 2015 Active contours driven by localizing region and edge–based intensity fitting energy with application to segmentation of the left ventricle in cardiac CT images Neurocomputing 156 199 | eng |
| dcterms.references | Olveres J, Nava R, Escalante-Ramírez B, Vallejo E and Kybic J 2017 Left ventricle Hermite–based segmentation Comput. Biol. Med. 87 236 | eng |
| dcterms.references | Bravo A, Vera M, Garreau M and Medina R 2011 Three–dimensional segmentation of ventricular heart chambers from multi–slice computerized tomography: An hybrid approach Digital Information and Communication Technology and Its Applications (DICTAP 2011), Communications in Computer and Information Science vol 166, ed Cherifi H, Zain J M, El-Qawasmeh E (Berlin: Springer) p 287 | eng |
| dcterms.references | Vera M, Bravo A, Garreau M and Medina R 2011 Similarity Enhancement for Automatic Segmentation of Cardiac Structures in Computed Tomography Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Boston: IEEE) p 8094 | eng |
| dcterms.references | Vera M et al. 2018 Automatic segmentation of subdural hematomas using a computational technique based on smart operators Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) (Porto: IEEE) | eng |
| dcterms.references | Yushkevich P, Piven J, Cody H, Ho S, Gee J and Gerig G 2005 User-Guided Level Set Segmentation of Anatomical Structures with ITK-SNAP The Insight Journal MICCAI Open-Source Workshop 1-9 | eng |
| dcterms.references | Chaudhury K and Ramakrishnan K 2007 Stability and convergence of the level set method in computer vision Pattern Recog. Let. 28 884 | eng |