Fusión de etiquetas basado en múltiples atlas usando ponderaciones locales supervisadas

  • David Cárdenas-Peña Universidad Nacional de Colombia
  • Eduardo Fernández Universidad Miguel Hernández
  • José M. Ferrández-Vicente Universidad Politécnica de Cartagena
  • German Castellanos-Domínguez Universidad Nacional de Colombia
Palabras clave: Segmentación de imágenes cerebrales, fusión de etiquetas, segmentación con múltiples atlas

Resumen

La segmentación automática de estructuras de interés en imágenes de resonancia magnética cerebral requiere esfuerzos significantes, debido a las formas complicadas, el bajo contraste y la variabilidad anatómica. Un aspecto que reduce el desempeño de la segmentación basada en múltiples atlas es la suposición de correspondencias uno-a-uno entre los voxeles objetivo y los del atlas. Para mejorar el desempeño de la segmentación, las metodologías de fusión de etiquetas incluyen información espacial y de intensidad a través de estrategias de votación ponderada a nivel de voxel. Aunque los pesos se calculan para un conjunto de atlas predefinido, estos no son muy eficientes en etiquetar estructuras intrincadas, ya que la mayoría de las formas de los tejidos no se distribuyen uniformemente en las imágenes. Este artículo propone una metodología de extracción de características a nivel de voxel basado en la combinación lineal de las intensidades de un parche. Hasta el momento, este es el primer intento de extraer características locales maximizando la función de alineamiento de kernel centralizado, buscando construir representaciones discriminativas, superar la complejidad de las estructuras, y reducir la influencia de los artefactos. Para validar los resultados, la estrategia de segmentación propuesta se compara contra la segmentación Bayesiana y la fusión de etiquetas basada en parches en tres bases de datos diferentes. Respecto del índice de similitud Dice, nuestra propuesta alcanza el más alto acierto (90.3% en promedio) con suficiente robusticidad ante los artefactos y respetabilidad apropiada.

Biografía del autor/a

David Cárdenas-Peña, Universidad Nacional de Colombia

PhD in Engineering, Signal Processing and Recognition Group

Eduardo Fernández, Universidad Miguel Hernández

PhD in Bioengineering, CIBER BBN

José M. Ferrández-Vicente, Universidad Politécnica de Cartagena

PhD in Informatics, DETCP

German Castellanos-Domínguez, Universidad Nacional de Colombia

PhD in Engineering, Signal Processing and Recognition Group

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Cómo citar
[1]
D. Cárdenas-Peña, E. Fernández, J. M. Ferrández-Vicente, y G. Castellanos-Domínguez, «Fusión de etiquetas basado en múltiples atlas usando ponderaciones locales supervisadas», TecnoL., vol. 20, n.º 39, pp. 209–225, may 2017.

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Publicado
2017-05-02
Sección
Artículos de investigación
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