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dc.contributor.author | Nava, Rodrigo | - |
dc.contributor.author | Escalante-Ramírez, Boris | - |
dc.contributor.author | Cristóbal, Gabriel | - |
dc.contributor.author | San José Estépar, Raúl | - |
dc.date.accessioned | 2015-02-27T08:46:20Z | - |
dc.date.available | 2015-02-27T08:46:20Z | - |
dc.date.issued | 2014 | - |
dc.identifier | doi: 10.1007/s11517-014-1139-9 | - |
dc.identifier | issn: 0140-0118 | - |
dc.identifier.citation | Medical and Biological Engineering and Computing 52: 393- 403 (2014) | - |
dc.identifier.uri | http://hdl.handle.net/10261/111447 | - |
dc.description | 11 pags.; 4 figs.; 6 tabs. | - |
dc.description.abstract | Chronic obstructive pulmonary disease (COPD) is a progressive and irreversible lung condition typically related to emphysema. It hinders air from passing through airpaths and causes that alveolar sacs lose their elastic quality. Findings of COPD may be manifested in a variety of computed tomography (CT) studies. Nevertheless, visual assessment of CT images is time-consuming and depends on trained observers. Hence, a reliable computer-aided diagnosis system would be useful to reduce time and inter-evaluator variability. In this paper, we propose a new emphysema classification framework based on complex Gabor filters and local binary patterns. This approach simultaneously encodes global characteristics and local information to describe emphysema morphology in CT images. Kernel Fisher analysis was used to reduce dimensionality and to find the most discriminant nonlinear boundaries among classes. Finally, classification was performed using the k-nearest neighbor classifier. The results have shown the effectiveness of our approach for quantifying lesions due to emphysema and that the combination of descriptors yields to a better classification performance. © 2014 International Federation for Medical and Biological Engineering. | - |
dc.description.sponsorship | This work has been partially sponsored by the grants UNAM PAPIIT IN113611, IG100814, and TEC2010-20307 from the Spanish Ministry of Economy. | - |
dc.publisher | Springer Nature | - |
dc.rights | closedAccess | - |
dc.subject | Emphysema | - |
dc.subject | COPD | - |
dc.subject | Texture analysis | - |
dc.subject | Kernel Fisher analysis | - |
dc.subject | Local binary patterns | - |
dc.subject | Gabor filters | - |
dc.title | Extended Gabor approach applied to classification of emphysematous patterns in computed tomography | - |
dc.type | artículo | - |
dc.identifier.doi | 10.1007/s11517-014-1139-9 | - |
dc.date.updated | 2015-02-27T08:46:20Z | - |
dc.description.version | Peer Reviewed | - |
dc.language.rfc3066 | eng | - |
dc.identifier.pmid | 24496558 | - |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | es_ES |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairetype | artículo | - |
item.grantfulltext | none | - |
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