A new approach for microcalcification detection using hybrid filtering and DBSCAN segmentation
DOI:
https://doi.org/10.61467/2007.1558.2026.v17i3.1151Keywords:
Breast cancer, Entropy, Clustering, DBSCAN, Cáncer de mama, microcalcificaciones, entropíaAbstract
In the field of medicine, various methods are available for detecting abnormalities in breast cancer diagnostic procedures. Mammography is the most commonly used method for microcalcification (MC) detection in the early stages. Moreover, the geometric characteristics of MCs evolve through different stages. Several studies have addressed the detection of MCs. In the first step, characteristic enhancement is used to isolate MCs from the whole image. In the final steps, classical clustering techniques are applied. Different studies focus on enhancing characteristics in the initial stage, employing separation techniques to distinguish the present objects from the entire image, and concluding with classical grouping techniques.
Spanish-language metadata / Metadatos en español
Título en español:
Un nuevo enfoque para la detección de microcalcificaciones mediante filtrado híbrido y segmentación DBSCAN
Resumen:
En el campo de la medicina, existen diversos métodos para detectar anomalías en los procedimientos de diagnóstico del cáncer de mama. La mamografía es el método más utilizado para la detección de microcalcificaciones (MC) en las etapas tempranas. Además, las características geométricas de las MC evolucionan a lo largo de las diferentes etapas. Varios estudios han abordado la detección de clústeres de objetos (MC). En la primera etapa, se utiliza el realce de características para aislar los MC de la imagen completa. En las etapas finales, se aplican técnicas clásicas de agrupamiento. Diversos estudios se centran en el realce de características en la etapa inicial, empleando técnicas de separación para distinguir los objetos presentes del resto de la imagen, y concluyendo con técnicas clásicas de agrupamiento.
Palabras Claves:
Cáncer de mama, microcalcificaciones, entropía, morfología, agrupamiento, DBSCAN
Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i3.1151
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