A computer-aided automated methodology for the detection and classification of occlusal caries from photographic color images. (1st July 2015)

• Record Type: Journal Article
• Authors: Berdouses, Elias D.; Koutsouri, Georgia D.; Tripoliti, Evanthia E.; Matsopoulos, George K.; Oulis, Constantine J.; Fotiadis, Dimitrios I.
• Is Part Of: Computers in biology and medicine. Volume 62(2015)
• Page Start: 119
• Page End: 135
• Publication Date: 2015-07-01
• Subjects: Occlusal caries -- Caries diagnosis -- ICDAS II -- Segmentation -- Feature extraction -- Feature selection -- Classification -- Random forests -- Digital imaging; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.04.016
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The aim of this work is to present a computer-aided automated methodology for the assessment of carious lesions, according to the International Caries Detection and Assessment System (ICDAS II), which are located on the occlusal surfaces of posterior permanent teeth from photographic color tooth images. The proposed methodology consists of two stages: (a) the detection of regions of interest and (b) the classification of the detected regions according to ICDAS ΙΙ. In the first stage, pre-processing, segmentation and post-processing mechanisms were employed. For each pixel of the detected regions, a 15×15 neighborhood is used and a set of intensity-based and texture-based features were extracted. A correlation based technique was applied to select a subset of 36 features which were given as input into the classification stage, where five classifiers (J48, Random Tree, Random Forests, Support Vector Machines and Naïve Bayes) were compared to conclude to the best one, in our case, to Random Forests. The methodology was evaluated on a set of 103 digital color images where 425 regions of interest from occlusal surfaces of extracted permanent teeth were manually segmented and classified, based on visual assessments by two experts. The methodology correctly detected 337 out of 340 regions in the detection stage with accuracy of detection 80%. For the classification stage an overall accuracy 83% is achieved. The proposed methodology provides an objective and fully automated caries diagnostic system for occlusal carious lesions with similar or better performance of a trained dentist taking into consideration the available medical knowledge. Highlights: An automated methodology for the diagnosis of occlusal caries is presented. Methodology can be a support to the dentists for making the final decision. Methodology is based on digital photography analysis. Methodology can incorporate new knowledge on caries diagnosis. Detection and classification (7 ICDAS classes) accuracy is 80% and 83%, respectively.