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Campo DC | Valor | Idioma |
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dc.contributor.author | Costa, Marly Guimarães Fernandes | - |
dc.contributor.author | Pinto, K. M.B. | - |
dc.contributor.author | Fujimoto, Luciana Botinelly Mendonça | - |
dc.contributor.author | Ogusku, Maurício Morishi | - |
dc.contributor.author | Costa Filho, Cícero Ferreira Fernandes | - |
dc.date.accessioned | 2020-06-15T21:35:56Z | - |
dc.date.available | 2020-06-15T21:35:56Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | https://repositorio.inpa.gov.br/handle/1/16720 | - |
dc.description.abstract | Bright-field microscopy of sputum samples is still the most widely used Tuberculosis (TB) diagnostic method in countries facing a high incidence of TB. However, this diagnostic method, because it is a visual analysis, requires attention and training of those who perform it, and presents high intra- and inter-observed variation. As a result, many research groups are working on methods of automatic detection of bacilli, aiming to automate this process. The fact that not all of the bacilli present in the examined microscope field are in focus increases the challenge faced by researchers in obtaining automatic methods of detecting bacilli. Fusion images can be a means of overcoming this problem, combining multiple images, from the same field, with diverse focuses into a single focused one. In this paper, we present a multi-focus image fusion method applied to conventional sputum smear microscopy images. The goal is to establish the best method to obtain an extended focus microscopy image where all bacilli present in the field are in focus. The proposed method was compared with three other techniques from the literature by using Variance and Multichannel QAB/F metrics. The proposed method exhibited the best balance of quality evidence (focus and preservation of information). © 2018 | en |
dc.language.iso | en | pt_BR |
dc.relation.ispartof | Volume 49, Pags. 289-297 | pt_BR |
dc.rights | Restrito | * |
dc.subject | Bacilli | en |
dc.subject | Image Processing | en |
dc.subject | Automatic Detection | en |
dc.subject | Bright-field Microscopy | en |
dc.subject | Depth Of Field | en |
dc.subject | Diagnostic Methods | en |
dc.subject | Light Fields | en |
dc.subject | Multi-focus | en |
dc.subject | Multifocus Image Fusion | en |
dc.subject | Tuberculosis | en |
dc.subject | Image Fusion | en |
dc.subject | Bacilli | en |
dc.subject | Bacterial Strain | en |
dc.subject | Bright-field Microscopy | en |
dc.subject | Color | en |
dc.subject | Controlled Study | en |
dc.subject | Human | en |
dc.subject | Image Analysis | en |
dc.subject | Image Processing | en |
dc.subject | Kruskal Wallis Test | en |
dc.subject | Multifocus Image Fusion | en |
dc.subject | Preservation | en |
dc.subject | Priority Journal | en |
dc.subject | Sputum Smear | en |
dc.subject | Tuberculosis | en |
dc.subject | Variance | en |
dc.subject | Wavelet Transformation | en |
dc.title | Multi-focus image fusion for bacilli images in conventional sputum smear microscopy for tuberculosis | en |
dc.type | Artigo | pt_BR |
dc.identifier.doi | 10.1016/j.bspc.2018.12.018 | - |
dc.publisher.journal | Biomedical Signal Processing and Control | pt_BR |
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