Una Técnica Bayesiana y de Varianza Mínima para Segmentación del Lumen Arterial en Imágenes de Ultrasonido

Sergio Rogelio Tinoco Martínez, Félix Calderon, Carlos Lara Álvarez, Jaime Carranza Madrigal


Las enfermedades cardiovasculares (ECVs) son la causa principal de decesos en el mundo entero. Basada en el ultrasonido, la valoración principal de las ECVs es la medición de la íntima-media carotídea y de la función endotelial humeral. En este trabajo se proponen mejoras a la metodología automática de detección del lumen arterial, fundamental en las pruebas referidas, presentada en (Calderon et al., 2013); basada en grafos y detección de bordes. Se propone un criterio bayesiano para segmentar el árbol de expansión mínima del grafo creado con los puntos intermedios entre los bordes. El lumen se localiza aplicando sobre las trayectorias segmentadas tres criterios: de longitud, de obscuridad y, el propuesto, de varianza mínima. En 294 sonografías el error promedio en la detección de la pared humeral cercana es 14.6 μm y desviación estándar 17.0 μm. En la pared lejana es 15.1 μm y desviación estándar 14.5 μm. Nuestra metodología mantiene el desempeño superior a los resultados en la literatura reciente que la metodología original presenta; superándola en exactitud general.

Palabras clave

Detección automática; ultrasonografía; carótida; humeral; lumen; bayesiano; varianza; grafos; ajuste polinomial

Texto completo:



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Mathematical Problems in Engineering  vol: 2015  primera página: 1  año: 2015  
doi: 10.1155/2015/201646

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