Cephalometric Analysis using PCA and SVM
نویسندگان
چکیده
Cephalometric analysis is the study of dental and skeletal relationship in the head. It depends on cephalometric radiography to study relationships between bony and soft tissue landmarks and can be used for diagnosis of facial growth abnormalities prior to treatment. Skeleton analysis consists of facial skeleton analysis, and mandibular and maxillary base analysis. In this work, landmarks needed for detecting skeletal abnormalities are selected from the digital image; Principal Component Analysis (PCA) is applied to the digital image for dimension reduction to get the desired feature vectors. The normalized feature vectors are trained and tested using an SVM classifier to detect the skeletal abnormalities. The performance measure such as accuracy, sensitivity and specificity were evaluated and the results are found to be satisfactory.
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