Cellular neural networks and dynamic enhancement for cephalometric landmarks detection

Authors:
D. Giordano;R. Leonardi;F. Maiorana;C. Spampinato
Affiliations:
Dipartimento Ingegneria Informatica e delle Telecomunicazioni, University of Catania, Catania, Italy;Policlinico Cittá Universitaria, Clinica Odontoiatrica II – University of Catania, Catania, Italy;Dipartimento Ingegneria Informatica e delle Telecomunicazioni, University of Catania, Catania, Italy;Dipartimento Ingegneria Informatica e delle Telecomunicazioni, University of Catania, Catania, Italy
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 5
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Abstract

Cephalometric landmarks detection is a knowledge intensive activity to identify on X-rays of the skull key points to perform measurements needed for medical diagnosis and treatment. We have elsewhere proposed CNNs (Cellular Neural Networks) to achieve an accuracy in automated landmarks detection suitable for clinical practice, and have applied the method for 8 landmarks located on the bone profile. This paper proposes and evaluates a CNNs approach augmented by local image dynamic enhancemet for other 3 landmarks that are notoriously difficult to locate; the advantages of this method in the landmark detection problem are pointed out.