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Proceedings Paper

Slope-chain-code-based characterization of Trypanosoma cruzi in blood smear images
Author(s): Angel Carrillo-Bermejo; Nidiyare Hevia-Montiel; Ernesto Bribiesca; Paulina Haro; Miguel Rosado-Vallado; Emilio Gonzalez-Mejia; J. Perez-Gonzalez
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Paper Abstract

Chagas disease (American trypanosomiasis) is an endemic parasitic disease in some areas of Latin America, about 16-18 million people are infected with the etiology agent of Chagas disease , Trypanosoma cruzi, and is transmitted to humans through triatomine insects commonly known as kissing bugs. One of the standard laboratory diagnosis during acute phase of the disease is by direct visualization of the parasite, the most common methods is the visualization in blood smear stained with some colorant. Trypanosoma cruzi uses several strategies to survive in different hosts which involves various morphological, biochemical, and genetic changes. Trypanosoma cruzi displays distinct morphology changes, which have not been fully characterized. The objective of this work is the morphological characterization of shape structures on blood smears. We proposed a high resolution chain code algorithm in bi-dimensional curves, which allows to discretize the contour with greater approximation to its real shape, and consequently obtain features in a objective way.

Paper Details

Date Published: 3 January 2020
PDF: 10 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300X (3 January 2020); doi: 10.1117/12.2541781
Show Author Affiliations
Angel Carrillo-Bermejo, Univ. de Antioquia (Mexico)
Nidiyare Hevia-Montiel, Univ. Nacional Autónoma de México (Mexico)
Ernesto Bribiesca, Univ. Nacional Autónoma de México (Mexico)
Paulina Haro, Univ. Autónoma de Yucatán (Mexico)
Miguel Rosado-Vallado, Univ. Autónoma de Yucatán (Mexico)
Emilio Gonzalez-Mejia, Tecnológico Nacional de México (Mexico)
J. Perez-Gonzalez, Univ. Nacional Autónoma de México (Mexico)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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