Abstract
In this study, we propose a scheme to delimit benign and malignant masses in breast ultrasound images. It consists of two stages: superpixels extraction by an Intuitionistic Fuzzy algorithm, which considers the local information of the image to develop a local segmentation; and clustering the superpixels by means of DBSCAN algorithm. The proposal does not require preprocessing of noise reduction inherent to this type of medical images or enhancement of features. The effectiveness of our proposal is verified by quantitative and qualitative results.
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Acknowledgements
The authors thank to CONACYT, as well as TecNM-CENIDET for their financial support through the project “Delimitación de masas sólidas malignas en mamografías mediante un algoritmo de nodos conectados con el menor ángulo polar".
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Mújica-Vargas, D., Luna-Álvarez, A., Rosales-Silva, A., Palacios-Cervantes, A. (2022). Delimitation of Benign and Malignant Masses in Breast Ultrasound by Clustering of Intuitionistic Fuzzy Superpixels Using DBSCAN Algorithm. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_32
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