<?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>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432019000100011</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2019.20n1.011</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A new method for Rician noise rejection in sparse representation]]></article-title>
<article-title xml:lang="es"><![CDATA[Un nuevo método para el rechazo de ruido Rician en representación escasa]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morera-Delfin]]></surname>
<given-names><![CDATA[Leandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Centro Nacional de Investigación y Desarrollo Tecnológico  ]]></institution>
<addr-line><![CDATA[ Morelos]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2019</year>
</pub-date>
<volume>20</volume>
<numero>1</numero>
<fpage>0</fpage>
<lpage>0</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432019000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432019000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432019000100011&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract One of the factors that affects the quality of medical images is noise in the acquisition process. Rician noise, for example, is present in MRI images and causes errors in measurements and interpretations of visual information. The objective of this work is to obtain high indexes of structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) through digital filtering of RICIAN noise. In the design, clusters of low coefficients are used to eliminate information redundancies, the probability density function (fdp) of the RICIAN noise to estimate signal levels and minimization by conjugate gradient to achieve a greater approximation to the real signal. The model is applied by filtering longitudinal sequences of MRI studies at T2 acquisition time affected with RICIAN noise in a controlled manner. Different models of noise filtering were implemented and tested on the same test sequence. The proposed method achieves an iterative approach to the real image. As a result, the SSIM and PSNR parameters improve in a magnitude of 0.02 and 0.3dB over the estimate with Gaussian fdp. The System has as limiting the effectiveness of the estimation for high signal levels due to the increase of the standard deviation in the fdp of the RICIAN noise in the aforementioned levels, however it manages to surpass the performance of current models within the state of the art. The proposed model has the novelty of linking the grouping of coefficients and the estimation by means of the fdp of the RICIAN noise. The system helps to avoid errors in measurements and interpretations of data affected by RICIAN noise, in particular in MRI studies. It is concluded that the fpd of RICIAN noise behaves like a good estimator in a digital filtering model with grouping of coefficients. Despite having better performance for medium and low signal levels, the proposed system manages to overcome the results obtained by other filtering models described within the state of the art.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Uno de los factores que afecta la calidad de las imágenes médicas es el ruido en el proceso de adquisición. El ruido Rician, por ejemplo, está presente en las imágenes de MRI y provoca errores en mediciones e interpretaciones de la información visual. El presente trabajo tiene como objetivo obtener elevados índices de similitud estructural (SSIM) y relación pico de señal sobre ruido (PSNR) mediante filtrado digital del ruido RICIAN. En el diseño se utilizan agrupamientos de coeficientes escasos para eliminar redundancias de información, la función de densidad de probabilidad (fdp) del ruido RICIAN para estimar niveles de señal y minimización mediante gradient conjugado para lograr mayor aproximación a la señal real. El modelo se aplica filtrando secuencias longitudinales de estudios MRI en tiempo de adquisición T2 afectadas con ruido RICIAN en forma controlada. Se implementaron diferentes modelos de filtrado de ruido y se probaron sobre la misma secuencia de prueba. El método propuesto logra una aproximación iterativa a la imagen real. Como resultado, los parámetros SSIM y PSNR mejoran en una magnitud de 0.02 y 0.3dB sobre la estimación con fdp gausiana. El Sistema tiene como limitante la efectividad de la estimación para altos niveles de señal debido al aumento de la desviación típica en la fdp del ruido RICIAN en los niveles mencionados, sin embargo, logra superar el desempeño de modelos actuales dentro del estado del arte. El modelo que se propone tiene la novedad de vincular el agrupamiento de coeficientes y la estimación mediante la fdp del ruido RICIAN. El sistema contribuye a evitar errores en mediciones e interpretaciones de datos afectados por ruido RICIAN, en particular, en estudios de MRI. Se concluye que la fdp del ruido RICIAN se comporta como un buen estimador en un modelo de filtrado digital con agrupación de coeficientes. A pesar de tener mejor desempeño para niveles medios y bajos de señal, el sistema propuesto logra superar los resultados obtenidos por otros modelos de filtrado descritos dentro del estado del arte.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Filtering]]></kwd>
<kwd lng="en"><![CDATA[Rician noise]]></kwd>
<kwd lng="en"><![CDATA[sparse representation]]></kwd>
<kwd lng="en"><![CDATA[clustering]]></kwd>
<kwd lng="en"><![CDATA[probability]]></kwd>
<kwd lng="es"><![CDATA[Filtrado]]></kwd>
<kwd lng="es"><![CDATA[ruido Rician]]></kwd>
<kwd lng="es"><![CDATA[representación escasa]]></kwd>
<kwd lng="es"><![CDATA[agrupamiento]]></kwd>
<kwd lng="es"><![CDATA[probabilidad]]></kwd>
</kwd-group>
</article-meta>
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