Wavelets Logarítmicas: Una Herramienta Efectiva para el Procesamiento de Mamogramas
Palabras clave:
Mejoramiento de Contraste, Transformada Wavelet, Transformada Wavelet Logarítmica, MamografíaResumen
El cáncer de mama es uno de los más frecuentes en este tipo de enfermedad y constituye la segunda causa de muerte en las mujeres. El éxito del tratamiento depende de la detección temprana de la enfermedad. La mamografía de rayos X es esencial para su diagnóstico. El reto del examen es obtener imágenes con buen contraste y resolución aplicando pequeñas dosis de radiación. La manipulación de los coeficientes de detalle en la Transformada Wavelet Discreta (TWD) bidimensional aplicada a las imágenes permite incrementar el contraste de las anomalías respecto a la regi ón circundante. También puede aplicarse TWD utilizando modelos no lineales del procesamiento de imágenes. Esta técnica se nombra Transformada Wavelet Logarítmica Discreta (TWL). En este artículo se propone un algoritmo para incrementar el contraste en mamografía empleando TWL. Para la experimentación se usó la base de datos MIAS y software basado en el lenguaje Python 2.7. Line profile, diagramas de caja, CII y DSM fueron utilizados como medidas de calidad del mejoramiento. Los resultados demostraron la efectividad del método propuesto y validaron que el método Correlación Local combinado con el modelo S–LIP obtuvo los mejores resultados medida–visualidad. En ocasiones, las medidas no reflejaron los resultados visuales debido a la definición de región de interés de MIAS.
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