Automatic identification of mycobacterium tuberculosis with conventional light microscopy
This article presents an automatic identification method of mycobacterium tuberculosis with conventional microscopy images based on Red and Green color channels using global adaptive threshold segmentation. Differing from fluorescence microscopy, in the conventional microscopy the bacilli are not ea...
| Autor principal: | Costa, Marly Guimarães Fernandes |
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| Outros Autores: | Costa Filho, Cícero Ferreira Fernandes, Sena, Juliana F., Salem, Júlia Ignez, Lima, Mari O. de |
| Grau: | Trabalho Apresentado em Evento |
| Idioma: | English |
| Publicado em: |
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
2020
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| Acesso em linha: |
https://repositorio.inpa.gov.br/handle/1/19999 |
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oai:repositorio:1-19999 |
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oai:repositorio:1-19999 Automatic identification of mycobacterium tuberculosis with conventional light microscopy Costa, Marly Guimarães Fernandes Costa Filho, Cícero Ferreira Fernandes Sena, Juliana F. Salem, Júlia Ignez Lima, Mari O. de Automation Electronic Data Interchange Microscopy, Fluorescence Adaptive Threshold Segmentations Automatic Identifications Green Colors Light Microscopies Microscopy Images Mycobacterium Tuberculosis Segmentation Methods Segmented Images Color Algorithm Artificial Intelligence Pattern Recognition, Automated Computer Assisted Diagnosis Cytology Evaluation Study Image Enhancement Methodology Microscopy Mycobacterium Tuberculosis Reproducibility Sensitivity And Specificity Algorithms Artificial Intelligence Image Enhancement Image Interpretation, Computer-assisted Microscopy Mycobacterium Tuberculosis Pattern Recognition, Automated Reproducibility Of Results Sensitivity And Specificity This article presents an automatic identification method of mycobacterium tuberculosis with conventional microscopy images based on Red and Green color channels using global adaptive threshold segmentation. Differing from fluorescence microscopy, in the conventional microscopy the bacilli are not easily distinguished from the background. The key to the bacilli segmentation method employed in this work is the use of Red minus Green (R-G) images from RGB color format. In this image, the bacilli appear as white regions on a dark background. Some artifacts are present in the (R-G) segmented image. To remove them we used morphological, color and size filters. The best sensitivity achieved was about 76.65%. The main contribution of this work was the proposal of the first automatic identification method of tuberculosis bacilli for conventional light microscopy. © 2008 IEEE. 2020-06-16T17:30:36Z 2020-06-16T17:30:36Z 2008 Trabalho Apresentado em Evento https://repositorio.inpa.gov.br/handle/1/19999 en Pags. 382-385 Restrito Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" |
| institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
| collection |
INPA-RI |
| language |
English |
| topic |
Automation Electronic Data Interchange Microscopy, Fluorescence Adaptive Threshold Segmentations Automatic Identifications Green Colors Light Microscopies Microscopy Images Mycobacterium Tuberculosis Segmentation Methods Segmented Images Color Algorithm Artificial Intelligence Pattern Recognition, Automated Computer Assisted Diagnosis Cytology Evaluation Study Image Enhancement Methodology Microscopy Mycobacterium Tuberculosis Reproducibility Sensitivity And Specificity Algorithms Artificial Intelligence Image Enhancement Image Interpretation, Computer-assisted Microscopy Mycobacterium Tuberculosis Pattern Recognition, Automated Reproducibility Of Results Sensitivity And Specificity |
| spellingShingle |
Automation Electronic Data Interchange Microscopy, Fluorescence Adaptive Threshold Segmentations Automatic Identifications Green Colors Light Microscopies Microscopy Images Mycobacterium Tuberculosis Segmentation Methods Segmented Images Color Algorithm Artificial Intelligence Pattern Recognition, Automated Computer Assisted Diagnosis Cytology Evaluation Study Image Enhancement Methodology Microscopy Mycobacterium Tuberculosis Reproducibility Sensitivity And Specificity Algorithms Artificial Intelligence Image Enhancement Image Interpretation, Computer-assisted Microscopy Mycobacterium Tuberculosis Pattern Recognition, Automated Reproducibility Of Results Sensitivity And Specificity Costa, Marly Guimarães Fernandes Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| topic_facet |
Automation Electronic Data Interchange Microscopy, Fluorescence Adaptive Threshold Segmentations Automatic Identifications Green Colors Light Microscopies Microscopy Images Mycobacterium Tuberculosis Segmentation Methods Segmented Images Color Algorithm Artificial Intelligence Pattern Recognition, Automated Computer Assisted Diagnosis Cytology Evaluation Study Image Enhancement Methodology Microscopy Mycobacterium Tuberculosis Reproducibility Sensitivity And Specificity Algorithms Artificial Intelligence Image Enhancement Image Interpretation, Computer-assisted Microscopy Mycobacterium Tuberculosis Pattern Recognition, Automated Reproducibility Of Results Sensitivity And Specificity |
| description |
This article presents an automatic identification method of mycobacterium tuberculosis with conventional microscopy images based on Red and Green color channels using global adaptive threshold segmentation. Differing from fluorescence microscopy, in the conventional microscopy the bacilli are not easily distinguished from the background. The key to the bacilli segmentation method employed in this work is the use of Red minus Green (R-G) images from RGB color format. In this image, the bacilli appear as white regions on a dark background. Some artifacts are present in the (R-G) segmented image. To remove them we used morphological, color and size filters. The best sensitivity achieved was about 76.65%. The main contribution of this work was the proposal of the first automatic identification method of tuberculosis bacilli for conventional light microscopy. © 2008 IEEE. |
| format |
Trabalho Apresentado em Evento |
| author |
Costa, Marly Guimarães Fernandes |
| author2 |
Costa Filho, Cícero Ferreira Fernandes Sena, Juliana F. Salem, Júlia Ignez Lima, Mari O. de |
| author2Str |
Costa Filho, Cícero Ferreira Fernandes Sena, Juliana F. Salem, Júlia Ignez Lima, Mari O. de |
| title |
Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| title_short |
Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| title_full |
Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| title_fullStr |
Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| title_full_unstemmed |
Automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| title_sort |
automatic identification of mycobacterium tuberculosis with conventional light microscopy |
| publisher |
Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" |
| publishDate |
2020 |
| url |
https://repositorio.inpa.gov.br/handle/1/19999 |
| _version_ |
1787143386242744320 |
| score |
11.755432 |