A computational approach for Leishmania genus protozoa detection in bone marrow samples from patients with visceral Leishmaniasis
This article reports a three-stage computational approach for the automatic detection of Leishmania protozoan in light microphotograph from bone marrow samples extracted from patients with visceral Leishmaniasis. The first stage corresponds to the pre-processing of the microscopy images, in which in...
- Autores:
-
Isaza-Jaimes, Angélica
Bérmudez, Valmore
Bravo, Antonio
Sierra Castrillo, Jhoalmis
Hernández Lalinde, Juan Diego
Fossi, Cleiver A.
Flórez, Anderson
Rodríguez, Johel E.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Simón Bolívar
- Repositorio:
- Repositorio Digital USB
- Idioma:
- eng
- OAI Identifier:
- oai:bonga.unisimon.edu.co:20.500.12442/9491
- Acceso en línea:
- https://hdl.handle.net/20.500.12442/9491
http://doi.org/10.5281/zenodo.4426403
http://saber.ucv.ve/ojs/index.php/rev_aavft/article/view/21140
- Palabra clave:
- Protozoan
Leishmania
micrographics
anisotropic diffusion
gradient operator
intensity profiles
Protozoario
micrografía
difusión anisotrópica
operador de gradiente
perfiles de intensidad
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
| Summary: | This article reports a three-stage computational approach for the automatic detection of Leishmania protozoan in light microphotograph from bone marrow samples extracted from patients with visceral Leishmaniasis. The first stage corresponds to the pre-processing of the microscopy images, in which initially a low-pass filter or softener was applied to attenuate the undesired information associated with the images and preserve the edges in the objects contained in the images. The pre-processing stage concluded with the applica tion of consistent gradient operators to the smoothed images to emphasise the changes of the intensities associated with the protozoa edges by determining the gradient module. In the second stage, a procedure-oriented to the selection of regions of interest that were candidates to contain parasites in the pre-processed images was developed, based on the intensity analysis associated with a set of intensity profiles selected from the smoothed images. In the final stage, each region of interest containing protozoa was analysed on the gradient module by a technique based on polar maps, to clas sify its content as a parasite of the genus Leishmania or not. The application of the proposed computational approach to a set of samples of patients with Visceral Leishmaniasis generated a recognition parasite percentage of approximately 80% |
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