Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears

Leishmaniasis is a complex group of diseases caused by obligate unicellular and intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These syndromes belong to three categ...

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Autores:
Salazar, J
Vera, M
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
Valbuena, O
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Simón Bolívar
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Repositorio Digital USB
Idioma:
eng
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oai:bonga.unisimon.edu.co:20.500.12442/5112
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https://hdl.handle.net/20.500.12442/5112
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dc.title.eng.fl_str_mv Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
spellingShingle Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title_short Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title_full Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title_fullStr Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title_full_unstemmed Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
title_sort Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears
dc.creator.fl_str_mv Salazar, J
Vera, M
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
Valbuena, O
dc.contributor.author.none.fl_str_mv Salazar, J
Vera, M
Huérfano, Y
Vera, M I
Gelvez-Almeida, E
Valbuena, O
description Leishmaniasis is a complex group of diseases caused by obligate unicellular and intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These syndromes belong to three categories: visceral leishmaniasis, cutaneous leishmaniasis and mucosal leishmaniasis. The visceral leishmaniasis is based on the reticuloendothelial system producing hepatomegaly, splenomegaly and lymphadenopathy. In the present article, a semiautomatic segmentation strategy is proposed to obtain the segmentations of the evolutionary shapes of visceral leishmaniasis called parasites, specifically of the type amastigote and promastigote. For this purpose, the optical microscopy images containing said evolutionary shapes, which are generated from a blood smear, are subjected to a process of transformation of the color intensity space into a space of intensity in gray levels that facilitate their subsequent preprocessing and adaptation. In the preprocessing stage, smoothing filters and edge detectors are used to enhance the optical microscopy images. In a complementary way, a segmentation technique that groups the pixels corresponding to each one of the parasites, presents in the considered images, is applied. The results reveal a high correspondence between the available manual segmentations and the semi-automatic segmentations which are useful for the characterization of the parasites. The obtained segmentations let us to calculate areas and perimeters associated with the parasites segmented. These results are very important in clinical context where both the area and perimeter calculated are vital for monitoring the development of visceral leishmaniasis.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-04-15T04:30:46Z
dc.date.available.none.fl_str_mv 2020-04-15T04:30:46Z
dc.type.eng.fl_str_mv article
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dc.type.driver.eng.fl_str_mv article
dc.identifier.issn.none.fl_str_mv 17426596
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/5112
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/5112
dc.language.iso.eng.fl_str_mv eng
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dc.format.mimetype.eng.fl_str_mv pdf
dc.publisher.eng.fl_str_mv IOP Publishing
dc.source.eng.fl_str_mv Journal of Physics: Conference Series
Vol. 1386 (2019)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1386/1/012135
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spelling Salazar, J6f1d932b-654d-42d9-bc5b-30b467b897d2Vera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eHuérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fVera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eGelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deValbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e92020-04-15T04:30:46Z2020-04-15T04:30:46Z201917426596https://hdl.handle.net/20.500.12442/5112Leishmaniasis is a complex group of diseases caused by obligate unicellular and intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These syndromes belong to three categories: visceral leishmaniasis, cutaneous leishmaniasis and mucosal leishmaniasis. The visceral leishmaniasis is based on the reticuloendothelial system producing hepatomegaly, splenomegaly and lymphadenopathy. In the present article, a semiautomatic segmentation strategy is proposed to obtain the segmentations of the evolutionary shapes of visceral leishmaniasis called parasites, specifically of the type amastigote and promastigote. For this purpose, the optical microscopy images containing said evolutionary shapes, which are generated from a blood smear, are subjected to a process of transformation of the color intensity space into a space of intensity in gray levels that facilitate their subsequent preprocessing and adaptation. In the preprocessing stage, smoothing filters and edge detectors are used to enhance the optical microscopy images. In a complementary way, a segmentation technique that groups the pixels corresponding to each one of the parasites, presents in the considered images, is applied. The results reveal a high correspondence between the available manual segmentations and the semi-automatic segmentations which are useful for the characterization of the parasites. The obtained segmentations let us to calculate areas and perimeters associated with the parasites segmented. These results are very important in clinical context where both the area and perimeter calculated are vital for monitoring the development of visceral leishmaniasis.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1386 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1386/1/012135Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smearsarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Longo D, Fauci A, Kasper D, Hauser S, Jameson J and Loscalzo J 2018 Harrison’s principles of internal medicine (USA: McGraw-Hill)Farahi M, Rabbani H, Talebi A, Sarrafzadeh O and Ensafi S 2015 Automatic segmentation of leishmania parasite in microscopic images using a modified cv level set method Proc. SPIE 9817 98170KVera M, Huérfano Y, Gelvez E, Valbuena O, Salazar J, Molina V, Vera M I, Salazar W and Sáenz F 2019 Segmentation of brain tumors using a semi-automatic computational strategy J. Phys.: Conf. Ser. 1160 012002Tan H, Jiang H, Dong A, Yang B and Zhang L 2014 Cv level set based cell image segmentation using color filter and morphology Proc. Int. Conf. on Information Sci. Electronics and Electrical Eng. 3(1) 1073Yang L, Meer P and Foran D 2005 Unsupervised segmentation based on robust estimation and color active contour models IEEE Trans. on Inf. Tech. in Biomedicine 9(3) 475Sadeghian F, Seman Z, Ramli A, Kahar B and Saripan M 2009 A framework for wbc segmentation in microscopic images using digital image processing Biological procedures online 11 196Górriz M, Aparicio A, Raventós B, Vilaplana V, Sayrol E and Lopez D 2018 Leishmaniasis parasite segmentation and classification using deep learning Proc. 10th International Conference: AMDO (Berlín Springer) 43956 53Farahi M, Rabbani H and Talebi A 2014 Automatic boundary extraction of leishman bodies in bone marrow samples from patients with visceral leishmaniasis. J. Isfahan. Med. Sch. 32(286) 726Koenderink J 1984 The structure of images Biol. Cybern. 50 363Zhigan N, Wenbin S and Xiong C 2015 Adhesion ore image separation method based on concave points matching Proc. International Conference on Information Technology and Intelligent Transportation Systems 2 (China: Springer)Ibañez L 2004 The ITK software guide (USA: Kitware Inc.)Vera M, Bravo A and Medina R 2011 Improving ventricle detection in 3d cardiac multislice computerized tomography images Proc. Computer Vision, Imaging and Computer Graphics. Theory and Applications. Communications in Computer and Information Science ed Richard P and Braz J 229. (Berlin: Springer Heidelberg)Vera M, Medina R, Del Mar A, Arellano J, Huérfano Y and Bravo A 2019 An automatic technique for left ventricle segmentation from msct cardiac volumes J. Phys.: Conf. 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