Comparación de Precisión de Espacios de Color en la Clasificación de Características de Células en Imágenes de Leucemia tipos ALL y MM
DOI:
https://doi.org/10.17488/RMIB.43.2.3Palabras clave:
PCA, Momentos estadísticos, Espacios de color, Imágenes de leucemiaResumen
Este estudio presenta una metodología para identificar el espacio de color que proporciona el mejor rendimiento en una aplicación de procesamiento de imágenes. Cuando las mediciones se realizan sin seleccionar el modelo de color adecuado, la precisión de los resultados se altera considerablemente. Esto es significativo en el procesamiento, principalmente cuando el diagnóstico se basa en imágenes de microscopía de células teñidas. Este trabajo muestra cómo la selección adecuada del modelo de color proporciona una mejor caracterización en dos tipos de cáncer, la leucemia linfoide aguda y el mieloma múltiple. La metodología utiliza imágenes de una base de datos pública. Primero, se segmentan los núcleos y luego se calculan los momentos estadísticos para la identificación de clases. Posteriormente, se realiza un análisis de componentes principales para reducir las características extraídas e identificar las más significativas. Por último, el modelo predictivo se evalúa utilizando el algoritmo k-vecinos más cercanos y una matriz de confusión. Para las imágenes utilizadas, los resultados mostraron que el espacio de color CIE L*a*b caracterizó mejor los tipos de cáncer analizados con una precisión promedio del 95,52%. Con una precisión del 91,81%, siguieron los espacios RGB y CMY. Los espacios HSI y HSV tuvieron una precisión del 87,86% y el 89,39%, respectivamente, y el peor desempeño fue la escala de grises con una precisión del 55,56%.
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