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Enhancement of nerve structure segmentation by a correntropy-based pre-image approach

Mejoramiento de la segmentación de estructuras nerviosas usando un enfoque de pre-imágenes basado en correntropía

dc.creatorGil-González, Julián
dc.creatorÁlvarez-Meza, Andrés A.
dc.creatorEcheverry-Correa, Julián D.
dc.creatorOrozco-Gutiérrez, Álvaro A.
dc.creatorÁlvarez-López, Mauricio A.
dc.date2017-05-02
dc.date.accessioned2021-03-18T21:06:50Z
dc.date.available2021-03-18T21:06:50Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/717
dc.identifier10.22430/22565337.717
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11710
dc.descriptionPeripheral Nerve Blocking (PNB) is a commonly used technique for performing regional anesthesia and managing pain. PNB comprises the administration of anesthetics in the proximity of a nerve. In this sense, the success of PNB procedures depends on an accurate location of the target nerve. Recently, ultrasound images (UI) have been widely used to locate nerve structures for PNB, since they enable a non-invasive visualization of the target nerve and the anatomical structures around it. However, UI are affected by speckle noise, which makes it difficult to accurately locate a given nerve. Thus, it is necessary to perform a filtering step to attenuate the speckle noise without eliminating relevant anatomical details that are required for high-level tasks, such as segmentation of nerve structures. In this paper, we propose an UI improvement strategy with the use of a pre-image-based filter. In particular, we map the input images by a nonlinear function (kernel). Specifically, we employ a correntropy-based mapping as kernel functional to code higher-order statistics of the input data under both nonlinear and non-Gaussian conditions. We validate our approach against an UI dataset focused on nerve segmentation for PNB. Likewise, our Correntropy-based Pre-Image Filtering (CPIF) is applied as a pre-processing stage to segment nerve structures in a UI. The segmentation performance is measured in terms of the Dice coefficient. According to the results, we observe that CPIF finds a suitable approximation for UI by highlighting discriminative nerve patterns.en-US
dc.descriptionEl bloqueo de nervios periféricos (PNB) es una técnica ampliamente usada para llevar a cabo anestesia regional en el manejo del dolor. El PNB aplica una sustancia anestésica en el área que rodea el nervio que se quiere intervenir, y su éxito depende de la localización exacta del mismo. Recientemente, las imágenes de ultrasonido (UI) se han utilizado para la localización de nervios periféricos en PNB ya que permiten una visualización no invasiva y directa del nervio y de las estructuras anatómicas alrededor de él; sin embargo, este tipo de imágenes están afectadas por ruido speckle, dificultando su delimitación exacta. De esta manera, es pertinente una etapa de filtrado para atenuar el ruido sin remover información anatómica importante para la segmentación. En este artículo se propone una estrategia para el mejoramiento de UI usando filtrado basado en pre-imágenes. En particular, las imágenes se mapean a un espacio de alta dimensionalidad a través de una función kernel. Específicamente, se emplea un mapeo basado en Correntropía con el fin de codificar estadísticos de orden superior de las imágenes bajo condiciones no-lineales y no-Gaussianas. El enfoque propuesto se valida en la segmentación de nervios para PNB. El enfoque de filtrado basado en pre-imágenes con Correntropía (CPIF) es usado como pre-procesamiento en tareas de segmentación de nervios sobre UI. El rendimiento de la segmentación es medida en términos del coeficiente Dice. De acuerdo con los resultados, CPIF encuentra una aproximación adecuada para las UI al asegurar la identificación de patrones discriminativos de estructuras nerviosas.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/717/698
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dc.rightshttps://creativecommons.org/licenses/by/3.0/deed.es_ESen-US
dc.sourceTecnoLógicas; Vol. 20 No. 39 (2017); 197-208en-US
dc.sourceTecnoLógicas; Vol. 20 Núm. 39 (2017); 197-208es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectNerve structure segmentationen-US
dc.subjectultrasond imagesen-US
dc.subjectpre-images approximationen-US
dc.subjectCorrentropyen-US
dc.subjectAnálisis de componentes principaleses-ES
dc.subjectCorrentropíaes-ES
dc.subjectFiltradoes-ES
dc.subjectFunciones Kerneles-ES
dc.subjectSegmentaciónes-ES
dc.titleEnhancement of nerve structure segmentation by a correntropy-based pre-image approachen-US
dc.titleMejoramiento de la segmentación de estructuras nerviosas usando un enfoque de pre-imágenes basado en correntropíaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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