<?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>2007-0705</journal-id>
<journal-title><![CDATA[Nova scientia]]></journal-title>
<abbrev-journal-title><![CDATA[Nova scientia]]></abbrev-journal-title>
<issn>2007-0705</issn>
<publisher>
<publisher-name><![CDATA[Universidad de La Salle Bajío A. C., Coordinación de Investigación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-07052019000200005</article-id>
<article-id pub-id-type="doi">10.21640/ns.v11i23.1902</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Determinación de la parábola de la vasculatura de la retina mediante un algoritmo computacional de segmentación]]></article-title>
<article-title xml:lang="en"><![CDATA[Determination of the parabola of the retinal vasculature using a segmentation computational algorithm]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Giacinti]]></surname>
<given-names><![CDATA[David Jaime]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cervantes Sánchez]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz Aceves]]></surname>
<given-names><![CDATA[Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández González]]></surname>
<given-names><![CDATA[Martha Alicia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[López Montero]]></surname>
<given-names><![CDATA[Luis Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Mexicano del Seguro Social Unidad Médica de Alta Especialidad T1 ]]></institution>
<addr-line><![CDATA[León Guanajuato]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro de Investigación en Matemáticas A.C.  ]]></institution>
<addr-line><![CDATA[Guanajuato Guanajuato]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<volume>11</volume>
<numero>23</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-07052019000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-07052019000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-07052019000200005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El análisis cuantitativo de la arquitectura de las venas temporales superior e inferior y su monitoreo sobre el tiempo puede facilitar el diagnóstico y tratamiento oportuno de la retinopatía diabética. En este trabajo se presenta un novedoso método que consiste de dos etapas correspondientes a la segmentación automática y modelado parabólico de las venas temporales superior e inferior en imágenes de fondo de ojo. En la primera etapa, el detector lineal multiescala (DLM) es empleado para detectar estructuras de tipo arterial en imágenes de la retina. Debido a que DLM es un método de realzado arterial, es necesario aplicar una estrategia de umbralización para clasificar pixeles de tipo arterial con respecto al fondo de la imagen, donde un valor de umbral determinado de forma experimental es comparado con cinco métodos de umbralización del estado del arte. En esta etapa, el método de segmentación propuesto es comparado con seis métodos especializados del estado del arte en términos de eficiencia de segmentación. En la segunda etapa, se desempeña un modelado parabólico mediante una estrategia de optimización utilizando un Algoritmo de Distribución Marginal Univariada sobre las arterias previamente segmentadas, y los resultados son comparados con dos métodos paramétricos del estado del arte y con las delineaciones realizadas por especialistas. Los resultados de segmentación arterial utilizando el detector lineal multiescala demostraron una alta eficiencia de segmentación obteniendo un valor de 0.9618 utilizando la base de datos DRIVE de imágenes de fondo de ojo. De igual forma, los resultados de modelado parabólico entregaron una eficiencia promedio de 0.825 con respecto a las delineaciones realizadas por especialistas oftalmólogos de las venas temporales superior e inferior. En base a los resultados de eficiencia y al tiempo computacional (5.62 segundos), el método propuesto puede considerarse como altamente apropiado para desempeñar diagnóstico asistido por computadora en el área de oftalmología.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Quantitative analysis of the architecture of the superior and inferior temporal retinal veins and their monitoring over time could facilitate the diagnosis and timely treatment of diabetic retinopathy. This paper presents a novel method consisting of two stages for automatic segmentation and parabolic modeling of the superior and inferior temporal arcade vessels in retinal fundus images. In the first stage, the Multiscale Line Detector (MLD) is used to detect vessel-like structures in the retinal images. Since the MLD, is a vessel enhancement method, a thresholding strategy has to be used to classify vessel and non-vessel pixels, where an experimental threshold value is compared with five state-of-the-art thresholding methods. In this stage, the proposed segmentation method is compared with six state-of-the-art specialized methods in terms of segmentation accuracy. In the second stage, a parabolic modeling using an optimization strategy based on the Univariate Marginal Distribution Algorithm (UMDA) is performed over the segmented vessels and the results are compared with two state-of-the-art parametric methods and with the ground-truth images outlined by specialists. The results of vessel segmentation using the multiscale line detector demonstrated a high segmentation accuracy with a 0.9618 value using the DRIVE database of retinal fundus images. In addition, the parabolic modeling results provided an average accuracy of 0.825 with the ground-truth of the superior and inferior temporal arcade vessels outlined by ophthalmologists. According to the accuracy and the computational time (5.62 seconds) results, the proposed method can be considered as highly appropriate to perform computer-aided diagnosis in Ophthalmology.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[algoritmo de estimación de distribución]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico asistido por computadora]]></kwd>
<kwd lng="es"><![CDATA[imágenes de fondo de ojo]]></kwd>
<kwd lng="es"><![CDATA[modelado parabólico]]></kwd>
<kwd lng="es"><![CDATA[segmentación automática]]></kwd>
<kwd lng="en"><![CDATA[estimation of distribution algorithms]]></kwd>
<kwd lng="en"><![CDATA[computer-aided diagnosis]]></kwd>
<kwd lng="en"><![CDATA[retinal fundus images]]></kwd>
<kwd lng="en"><![CDATA[parabolic modeling]]></kwd>
<kwd lng="en"><![CDATA[automatic segmentation]]></kwd>
</kwd-group>
</article-meta>
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