<?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>1684-1859</journal-id>
<journal-title><![CDATA[Revista Cubana de Informática Médica]]></journal-title>
<abbrev-journal-title><![CDATA[RCIM]]></abbrev-journal-title>
<issn>1684-1859</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Ciencias Médicas de La Habana]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1684-18592020000200007</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Técnicas de segmentación y procesamiento para la detección de Carcinomas Renales en imágenes de Tomografía Abdominal]]></article-title>
<article-title xml:lang="en"><![CDATA[Segmentation and processing techniques for the detection of Renal Carcinomas in Abdominal Tomography images]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Orellana García]]></surname>
<given-names><![CDATA[Arturo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García Portal]]></surname>
<given-names><![CDATA[Luis Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciencias Informáticas Centro de Informática Médica ]]></institution>
<addr-line><![CDATA[La Habana ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2020</year>
</pub-date>
<volume>12</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1684-18592020000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1684-18592020000200007&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1684-18592020000200007&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Una de las campañas más reconocidas en el mundo es la lucha contra el cáncer, siendo el sistema renal uno de los más afectados por esta patología. El carcinoma de células renales (CCR), el más común de cáncer renal en los adultos, representa la sexta causa de muerte por cáncer. Debido al aumento en el uso de las técnicas de diagnóstico por imagen, las lesiones renales pueden ser diagnosticadas en forma incidental aproximadamente en 50% de los casos. Cuba apuesta por el uso de la tecnología en la salud y en la Universidad de las Ciencias Informáticas (UCI) se ha desarrollado un sistema para el almacenamiento, transmisión y visualización de imágenes médicas (XAVIA PACS), el cual se encuentra implantado en varios hospitales del país, pero no cuenta con alternativas para realizar la detección del CCR en imágenes tomográficas, haciendo más lento el diagnóstico, lo que se traduce en menos posibilidades para el paciente. La presente investigación tiene como objetivo realizar un análisis sobre las principales técnicas de segmentación y procesamiento para la detección de carcinomas renales en imágenes de tomografías abdominal, que propicie a los equipos de desarrollo contar con la base teórica necesaria para enfrentar el problema en cuestión. Para ello se realizó un análisis documental sobre trabajos relacionados con la temática y que propician soluciones al problema. Se estudiaron algoritmos y técnicas computacionales efectivas para la segmentación y procesamiento de imágenes abdominales. Como resultado de la investigación se obtuvieron los algoritmos más acordes para el sistema XAVIA PACS y el contexto médico cubano.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT One of the most recognized campaigns in the world is the fight against cancer, the kidney system being one of the most affected by this pathology. Renal cell carcinoma (RCC), the most common form of kidney cancer in adults, represents the sixth leading cause of cancer death. Due to the increased use of diagnostic imaging techniques, kidney injuries can be diagnosed incidentally in approximately 50% of cases. Cuba is committed to the use of technology in health and a system for the storage, transmission and display of medical images (XAVIA PACS) has been developed at the University of Computer Sciences (UCI), which is implanted in several hospitals of the country, but it does not have alternatives to detect RCC in tomographic images, slowing down the diagnosis, which translates into fewer possibilities for the patient. The objective of this research is to carry out an analysis on the main segmentation and processing techniques for the detection of renal carcinomas in abdominal tomography images, which provides development teams with the theoretical basis necessary to face the problem in question. For this, a documentary analysis was carried out on works related to the subject and that provide solutions to the problem. Algorithms and effective computational techniques for the segmentation and processing of abdominal images were studied. As a result of the research, the most suitable algorithms for the XAVIA PACS system and the Cuban medical context were obtained.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis de algoritmos]]></kwd>
<kwd lng="es"><![CDATA[carcinoma renal]]></kwd>
<kwd lng="es"><![CDATA[transformada Watersheds]]></kwd>
<kwd lng="es"><![CDATA[informática médica]]></kwd>
<kwd lng="es"><![CDATA[XAVIA PACS]]></kwd>
<kwd lng="en"><![CDATA[algorithm analysis]]></kwd>
<kwd lng="en"><![CDATA[renal carcinoma]]></kwd>
<kwd lng="en"><![CDATA[Watersheds Transform]]></kwd>
<kwd lng="en"><![CDATA[medical informatics]]></kwd>
<kwd lng="en"><![CDATA[XAVIA PACS]]></kwd>
</kwd-group>
</article-meta>
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