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The purpose of this study is to investigate computer vision and machine learning methods for classification of orthodontic images in order to provide orthodontists with a solution for multi-class classification of patients’ images to evaluate the evolution of their treatment. Of which, we proposed three algorithms based on extracted features, such as facial features and skin colour using YCbCrcolour space, assigned to nodes of a decision tree to classify orthodontic images: an algorithm for intra-oral images, an algorithm for mould images and an algorithm for extra-oral images. Then, we compared our method by implementing the Local Binary Pattern (LBP) algorithm to extract textural features from images. After that, we applied the principal component analysis (PCA) algorithm to optimize the redundant parameters in order to classify LBP features with six classifiers; Quadratic Support Vector Machine (SVM), Cubic SVM, Radial Basis Function SVM, Cosine K-Nearest Neighbours (KNN), Euclidian KNN, and Linear Discriminant Analysis (LDA). The presented algorithms have been evaluated on a dataset of images of 98 different patients, and experimental results demonstrate the good performances of our proposed method with a high accuracy compared with machine learning algorithms. Where LDA classifier achieves an accuracy of 84.5%.

Numerous clinical studies have demonstrated the influence of the size, number and shape of pores into calcium phosphate ceramics on the process of bone regeneration.The main objective of this study is to determine the microstructure, the morphological characteristics and classes of pores of the prepared hydroxyapatite bioceramic using an adaptive method based on the mathematical morphological operations. The study was carried out using X-ray microtomography and Scanning Electron Microscopy images. The conventional method of openings alone presents limitation of calculation and not sufficient to achieve our objective. The proposed method allowed us to extract local characteristics and calculate precisely the morphological parameters while preserving the original volume of pores. The number and classes of pores with their size, surface of contact of the component and the number of connected pores to each pore were calculated. The method is subjectively and quantitatively evaluated using different computed phantoms and its efficiency is clearly demonstrated through the different reports and measurements generated. The proposed method can have interesting applications in the characterization of porous materials used in the medical field or in other sectors.

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