<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1727-897X</journal-id>
<journal-title><![CDATA[MediSur]]></journal-title>
<abbrev-journal-title><![CDATA[Medisur]]></abbrev-journal-title>
<issn>1727-897X</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de Cienfuegos, Centro Provincial de Ciencias Médicas, Provincia de Cienfuegos.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1727-897X2022000200243</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Conteo de bacterias y levaduras en imágenes digitales]]></article-title>
<article-title xml:lang="en"><![CDATA[Counting of bacteria and yeasts in digital images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peña Martín]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Alvarado Capó]]></surname>
<given-names><![CDATA[Yelenys]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orozco Morales]]></surname>
<given-names><![CDATA[Rubén]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pichardo]]></surname>
<given-names><![CDATA[Tatiana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Abreu López]]></surname>
<given-names><![CDATA[Ailet]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Departamento de Control Automático. Universidad Central Marta Abreu de Las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Laboratorio de Microbiología Aplicada. Instituto de Biotecnología de las Plantas.  ]]></institution>
<addr-line><![CDATA[Villa Clara. ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>20</volume>
<numero>2</numero>
<fpage>243</fpage>
<lpage>256</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1727-897X2022000200243&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1727-897X2022000200243&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1727-897X2022000200243&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Fundamento:  en los laboratorios de microbiología, la identificación y conteo de microorganismos es un procedimiento habitual. Aunque existen en el mercado equipos que posibilitan su realización de manera automática o semiautomática, son muy costosos, por lo cual esta tarea, difícil e irritante para los ojos, la siguen realizando los expertos de manera tradicional mediante la observación de las muestras en los microscopios, con la consiguiente variabilidad entre ellos.  Objetivo:  proponer un nuevo método para el conteo de bacterias y levaduras en imágenes digitales, bajo diferentes magnificaciones, tomadas a bioproductos de origen microbiano obtenidos por fermentación.  Métodos:  el sensor empleado para la toma de imágenes de las muestras fue una cámara digital modelo HDCE-X, con un sensor CMOS de ½", con una resolución de 2592 píxeles por 1944 píxeles (5 Mp). Se emplearon dos tipos de magnificaciones: magnificación 40x (PL40, 0.65 apertura numérica and 0.17 de distancia de trabajo) y magnificación 100x (HI plan 100/1.25 con inmersión de aceite). El método propuesto se basa en técnicas de procesamiento digital de imágenes, utilizando herramientas como la detección de contornos, operaciones morfológicas y análisis estadístico, y fue desarrollado en lenguaje Python con empleo de la biblioteca OpenCV.  Resultados:  la detección y conteo de bacterias se logró con una exactitud y precisión aceptable, en ambos casos por encima de 0,95; no en el caso de las levaduras cuya exactitud y precisión fueron menores, alrededor de 0,78 y 0,86 respectivamente. Se proponen flujos de trabajo basados en técnicas de procesamiento digital de imágenes, fundamentalmente en detección de contornos, operaciones morfológicas y análisis estadístico.  Conclusiones:  el método posee una efectividad aceptable para el contexto y depende de las características que presenten las imágenes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Background: In microbiology laboratories, the identification and counting of microorganisms is a common procedure; and although there is a variety of equipment on the market that possibility to carry out these processes automatically or semi-automatically, it is usually expensive to many laboratories. These are some of the reasons why this arduous and difficult task is still performed in many laboratories by experts in the traditional way, through the observation of samples in microscope, consuming a great time and having variations in the results between experts.  Objective:  The present work aims to propose a new method for counting bacteria and yeasts in digital images, taken under different magnifications, of microbial bioproducts obtained by fermentation.  Methods:  The sensor used to take images of the samples was a digital camera model HDCE-X, with a ½" CMOS sensor, with a resolution of 2592 pixels by 1944 pixels (5 Mp). Two types of magnifications were used: 40x magnification (PL40, 0.65 numerical aperture and 0.17 working distance) and 100x magnification (HI plan 100/1.25 with oil immersion). The proposed method is based on digital image processing technics, using tools as contour detection, morphological operations and statistical analysis, and was developed in Python language using the OpenCV library. The work also presents a comparison with the results obtained using ImageJ software for the same purpose.  Results:  the detection and count of bacteria was achieved with an acceptable accuracy and precision, in both cases above 0.95; not in the case of yeasts whose accuracy and precision was lower, around 0.78 for accuracy and 0.86 for precision. Workflows based on digital image processing techniques are proposed, using tools as contour detection, morphological operations and statistical analysis.  Conclusions: the method has an acceptable effectiveness for the context and depends on the characteristics presented by the images.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[bacterias]]></kwd>
<kwd lng="es"><![CDATA[levaduras]]></kwd>
<kwd lng="es"><![CDATA[técnicas microbiológicas]]></kwd>
<kwd lng="es"><![CDATA[procesamiento de imagen asistido por computador]]></kwd>
<kwd lng="en"><![CDATA[bacteria]]></kwd>
<kwd lng="en"><![CDATA[yeasts]]></kwd>
<kwd lng="en"><![CDATA[microbiological techniques]]></kwd>
<kwd lng="en"><![CDATA[image processing, computer-assisted]]></kwd>
</kwd-group>
</article-meta>
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