Automated classification of periodontal disease using bitewing radiographs.
The feasibility of applying a prototype, computer-based pattern recognition system to the objective classification of periodontal disease using dental radiographs was tested. Twenty-nine observer-classified bitewing radiographs, representing seven individuals with varying grades of periodontal disease, were selected. The radiographs were digitized using a computer-controlled TV camera. Mathematical features of these radiographs were interactively extracted using a digital image processing system (International Imaging Systems Model 75 and System/575). The features extracted from these radiographs included the brightness levels of cortical and trabecular bone and ratios of bone-loss to linear-crown height. Twenty-eight mathematically defined features (variables) were determined for each radiograph. Stepwise linear discriminant analysis used these features to classify subjects based on the presence and extent of periodontal disease. This pattern recognition system was able to grade periodontal disease in our test series with percentages of correct classifications ranging from 78.8% to 91%. This technology is particularly applicable to the development of morbidity and activity indices for periodontal diseases.
Hildebolt
C F
CF
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63130.
Vannier
M W
MW
D000539:Alveolar Process / Q000000981:diagnostic imaging; D001862:Bone Resorption / Q000145:classification / Q000000981:diagnostic imaging; D003201:Computers; D003743:Dental Enamel / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated; D010510:Periodontal Diseases / Q000145:classification* / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D012984:Software