<?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>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322018000100113</article-id>
<article-id pub-id-type="doi">10.17488/rmib.39.1.10</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A fuzzy approach for feature extraction of brain tissues in Non-Contrast CT]]></article-title>
<article-title xml:lang="es"><![CDATA[Un enfoque difuso para la extracción de características de tejidos cerebrales en TC no contrastada]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gordillo-Castillo]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Davis-Ortiz]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aymerich]]></surname>
<given-names><![CDATA[F. X.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mejía-Muñoz]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García-Quintero]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López-Córdova]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Andrade-Luján]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Politécnica de Cataluña  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>España</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Colegio de Especialistas en Radiología e Imagen de Ciudad Juárez  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2018</year>
</pub-date>
<volume>39</volume>
<numero>1</numero>
<fpage>113</fpage>
<lpage>120</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322018000100113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322018000100113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322018000100113&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: In neuroimaging, brain tissue segmentation is a fundamental part of the techniques that seek to automate the detection of pathologies, the quantification of tissues or the evaluation of the progress of a treatment. Because of its wide availability, lower cost than other imaging techniques, fast execution and proven efficacy, Non-contrast Cerebral Computerized Tomography (NCCT) is the most used technique in emergency room for neuroradiology examination, however, most research on brain segmentation focuses on MRI due to the inherent difficulty of brain tissue segmentation in NCCT. In this work, three brain tissues were characterized: white matter, gray matter and cerebrospinal fluid in NCCT images. Feature extraction of these structures was made based on the radiological attenuation index denoted by the Hounsfield Units using fuzzy logic techniques. We evaluated the classification of each tissue in NCCT images and quantified the feature extraction technique in synthetic images from real tissues with a sensitivity of 92% and a specificity of 96% for images from cases with slice thickness of 1 mm, and 96% and 98% respectively for those of 1.5 mm, demonstrating the ability of the method as feature extractor of brain tissues.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: En neuroimagen, la segmentación de tejidos cerebrales es una parte fundamental de las técnicas que buscan automatizar la detección de patologías, la cuantificación de tejidos o la evaluación del progreso de un tratamiento. Debido a su amplia disponibilidad, menor costo que otras te&#769;cnicas de imagen, ra&#769;pida ejecucio&#769;n y eficacia probada, la tomografi&#769;a computarizada cerebral sin contraste (TCNC) es la técnica mayormente utilizada en emergencias para el examen neurorradiolo&#769;gico, sin embargo, la dificultad inherente que representa la segmentación de los tejidos cerebrales, hace que la mayoría de las investigaciones sobre la segmentación del cerebro se centren en la resonancia magnética. En este trabajo se realizó la caracterización de tres tejidos cerebrales: sustancia blanca, sustancia gris y líquido cefalorraquídeo en imágenes TCNC. Dichas estructuras fueron caracterizadas con base en el índice de atenuación radiológica denotadas por las Unidades Hounsfield utilizando técnicas de lógica difusa. Se evaluó la caracterización de cada tejido en diversos cortes de TCNC y se cuantificó la técnica de extracción de características en imágenes sintéticas a partir de tejidos reales con una sensibilidad de 92% y una especificidad de 96% para tejidos en cortes de 1 mm de grosor y 96% y 98% para los de 1.5 mm demostrando la habilidad del método como extractor de características de los tejidos cerebrales.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[brain tissue segmentation]]></kwd>
<kwd lng="en"><![CDATA[NCCT]]></kwd>
<kwd lng="en"><![CDATA[fuzzy logic]]></kwd>
<kwd lng="es"><![CDATA[segmentación de tejidos cerebrales]]></kwd>
<kwd lng="es"><![CDATA[TCNC]]></kwd>
<kwd lng="es"><![CDATA[lógica difusa]]></kwd>
</kwd-group>
</article-meta>
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