<?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>S0188-95322022000200101</article-id>
<article-id pub-id-type="doi">10.17488/rmib.43.2.3</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Comparison of Accuracy of Color Spaces in Cell Features Classification in Images of Leukemia types ALL and MM]]></article-title>
<article-title xml:lang="es"><![CDATA[Comparación de Precisión de Espacios de Color en la Clasificación de Características de Células en Imágenes de Leucemia tipos ALL y MM]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Espinoza-Del Angel]]></surname>
<given-names><![CDATA[Cinthia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Femat-Diaz]]></surname>
<given-names><![CDATA[Aurora]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Querétaro  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2022</year>
</pub-date>
<volume>43</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322022000200101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322022000200101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322022000200101&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This study presents a methodology for identifying the color space that provides the best performance in an image processing application. When measurements are performed without selecting the appropriate color model, the accuracy of the results is considerably altered. It is significant in computation, mainly when a diagnostic is based on stained cell microscopy images. This work shows how the proper selection of the color model provides better characterization in two types of cancer, acute lymphoid leukemia, and multiple myeloma. The methodology uses images from a public database. First, the nuclei are segmented, and then statistical moments are calculated for class identification. After, a principal component analysis is performed to reduce the extracted features and identify the most significant ones. At last, the predictive model is evaluated using the k-nearest neighbor algorithm and a confusion matrix. For the images used, the results showed that the CIE L*a*b color space best characterized the analyzed cancer types with an average accuracy of 95.52%. With an accuracy of 91.81%, RGB and CMY spaces followed. HSI and HSV spaces had an accuracy of 87.86% and 89.39%, respectively, and the worst performer was grayscale with an accuracy of 55.56%.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este estudio presenta una metodología para identificar el espacio de color que proporciona el mejor rendimiento en una aplicación de procesamiento de imágenes. Cuando las mediciones se realizan sin seleccionar el modelo de color adecuado, la precisión de los resultados se altera considerablemente. Esto es significativo en el procesamiento, principalmente cuando el diagnóstico se basa en imágenes de microscopía de células teñidas. Este trabajo muestra cómo la selección adecuada del modelo de color proporciona una mejor caracterización en dos tipos de cáncer, la leucemia linfoide aguda y el mieloma múltiple. La metodología utiliza imágenes de una base de datos pública. Primero, se segmentan los núcleos y luego se calculan los momentos estadísticos para la identificación de clases. Posteriormente, se realiza un análisis de componentes principales para reducir las características extraídas e identificar las más significativas. Por último, el modelo predictivo se evalúa utilizando el algoritmo k-vecinos más cercanos y una matriz de confusión. Para las imágenes utilizadas, los resultados mostraron que el espacio de color CIE L*a*b caracterizó mejor los tipos de cáncer analizados con una precisión promedio del 95,52%. Con una precisión del 91,81%, siguieron los espacios RGB y CMY. Los espacios HSI y HSV tuvieron una precisión del 87,86% y el 89,39%, respectivamente, y el peor desempeño fue la escala de grises con una precisión del 55,56%.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[PCA]]></kwd>
<kwd lng="en"><![CDATA[Statistical moments]]></kwd>
<kwd lng="en"><![CDATA[Color spaces]]></kwd>
<kwd lng="en"><![CDATA[Leukemia images]]></kwd>
<kwd lng="es"><![CDATA[PCA]]></kwd>
<kwd lng="es"><![CDATA[Momentos estadísticos]]></kwd>
<kwd lng="es"><![CDATA[Espacios de color]]></kwd>
<kwd lng="es"><![CDATA[Imágenes de leucemia]]></kwd>
</kwd-group>
</article-meta>
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