<?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>S1727-897X2022000200257</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Segmentación del hígado en imágenes de tomografía computarizada]]></article-title>
<article-title xml:lang="en"><![CDATA[Liver´s segmentation on computed tomography images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Yusta Gómez]]></surname>
<given-names><![CDATA[Melanie]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Díaz]]></surname>
<given-names><![CDATA[Marlen]]></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[Plasencia Hernández]]></surname>
<given-names><![CDATA[Xiomara]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Central Marta Abreu de las Villas  ]]></institution>
<addr-line><![CDATA[Villa Clara ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hospital Provincial Universitario Oncológico Dr. Celestino Hernández Robau  ]]></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>257</fpage>
<lpage>271</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1727-897X2022000200257&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1727-897X2022000200257&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1727-897X2022000200257&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Fundamento: la segmentación del hígado utilizando datos de tomografía computarizada es el primer paso para el diagnóstico de enfermedades hepáticas. Actualmente la segmentación de estructuras y órganos, basado en imágenes, que se realiza en los hospitales del país, dista de tener los niveles de precisión que se obtienen de los modernos sistemas 3D, por lo que se requiere buscar alternativas viables utilizando el PDI sobre ordenador.  Objetivo: determinar una variante eficaz y eficiente desde el punto de vista computacional en condiciones de rutina hospitalaria, para la segmentación de imágenes hepáticas con fines clínicos.  Métodos: se compararon dos métodos modernos de segmentación (Graph Cut y EM/MPM) aplicándolos sobre imágenes de tomografía de hígado. Se realizó un análisis evaluativo y estadístico de los resultados obtenidos en la segmentación de las imágenes a partir de los coeficientes de Dice, Vinet y Jaccard.  Resultados:  con el método Graph Cut, en todos los casos, se segmentó la región deseada, incluso cuando la calidad de las imágenes era baja, se observó gran similitud entre la imagen segmentada y la máscara de referencia. El nivel de detalles visuales es bueno y la reproducción de bordes permanece fiel a la máscara de referencia. La segmentación de las imágenes por el método de EM/MPM, no siempre fue satisfactoria.  Conclusiones:  el método de segmentación Graph Cut obtuvo mayor precisión para segmentar imágenes de hígado.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Background: liver segmentation using computed tomography data is the first step for the diagnosis of liver diseases. Currently, the segmentation of structures and organs, based on images, which is carried out in the country's hospitals, is far from having the levels of precision obtained from modern 3D systems, it is necessary to search for viable alternatives using the PDI on a computer.  Objective: to determine an effective and efficient variant from the computational point of view in routine hospital conditions, for the segmentation of liver images for clinical purposes.  Methods: Two modern segmentation methods (Graph Cut and EM/MPM) were compared by applying them to liver tomography images. An evaluative and statistical analysis of the results obtained in the segmentation of the images from the Dice, Vinet and Jaccard coefficients was carried out.  Results: with the Graph Cut method, in all cases, the desired region was segmented, even when the quality of the images was low, great similarity was observed between the segmented image and the reference mask. The level of visual detail is good, and edge reproduction remains true to the reference skin. Image segmentation by the EM/MPM method was not always satisfactory.  Conclusions: the Graph Cut segmentation method obtained greater precision to segment liver images.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[procesamiento de imagen asistido por computador]]></kwd>
<kwd lng="es"><![CDATA[tomografía computarizada por rayos X]]></kwd>
<kwd lng="es"><![CDATA[hígado]]></kwd>
<kwd lng="en"><![CDATA[image processing, computer-assisted, tomography, X-Ray computed]]></kwd>
<kwd lng="en"><![CDATA[liver]]></kwd>
</kwd-group>
</article-meta>
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