Dalel Ben Ismail1, Hela Elmannai2,*, Souham Meshoul2, Mohamed Saber Naceur1
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2219-2236, 2023, DOI:10.32604/iasc.2023.028944
Abstract Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies… More >
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