Cuevas E, Osuna-Enciso V, Oliva D, Wario F, Zaldivar D

Idioma: Español
Referencias bibliográficas: 55
Paginas: 119-134
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RESUMEN

Los glóbulos blancos (GB) o leucocitos juegan un papel importante en el diagnóstico de distintas enfermedades. Aunque técnicas de procesamiento digital de imágenes han ayudado parcialmente al análisis de tales células, prevalecen distintas complicaciones en la detección de GB, debido a variaciones en forma, tamaño, así como iluminaciones no adecuadas inherentes a la preparación del frotis sanguíneo. Por otra parte, los sistemas inmunes artificiales (SIA), basados en la manera en que el sistema inmunológico natural optimiza sus funciones para la detección de antígenos, han sido aplicados con éxito en la solución de distintos problemas de optimización, produciendo resultados superiores comparados con las técnicas clásicas. El enfoque más usado de SIA es el algoritmo de selección clonal (ASC), el cual permite proliferar a aquellas soluciones (anticuerpos) que mejor resuelven a una determinada función de desempeño (antígeno). Este artículo presenta un algoritmo para la segmentación, detección y medición de leucocitos en imágenes de frotis sanguíneo. El algoritmo usa dos sistemas ASC, uno para segmentación y el otro para detección de leucocitos. El problema de detección es considerado en este trabajo como un problema de optimización entre el leucocito y la forma circular que mejor lo aproxime. El algoritmo utiliza la codificación de tres puntos de la imagen de bordes para modelar los leucocitos candidatos. Una función de costo evalúa si tales leucocitos candidatosestán realmente en la imagen. Siguiendo los valores de tal función de costo, el grupo de leucocitos candidatos es modificado usando ASChasta aproximarse a los leucocitos reales presentes en la imagen de bordes. Los resultados obtenidos en comparación a otros algoritmos usados para la misma tarea validan la eficiencia de esta propuesta en exactitud, velocidad y robustez.

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