Hicham RIRI - Academia.edu (original) (raw)
Papers by Hicham RIRI
Advances in Intelligent Systems and Computing, 2020
International Journal of Electrical and Computer Engineering (IJECE), 2020
The purpose of this study is to investigate computer vision and machine learning methods for clas... more 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), Euclid...
International Orthodontics, 2018
Objective: The aim of this study was to develop a new method for an automatic detection of refere... more Objective: The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. Materials and methods: A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. Results: The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32 mm. Discussion: In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric
International Orthodontics, 2018
2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), 2016
Recognition and classification of images have a wide field of applications, especially in medical... more Recognition and classification of images have a wide field of applications, especially in medical images. In order to provide orthodontists a solution for classification of patients' images to evaluate the evolution of their treatment, we need to use latest efficient technics of classification. In this paper, we propose an algorithm based on a decisional tree to classify and recognize 19 types of dental images. This hierarchical representation can be interpreted as a set of hierarchical types stored in leafs tree structure. By using several extracted features from color images acquired with a digital camera and grayscale images acquired by x-ray scanner. Such as facial features and skin color using YCbCr color-space. The proposed technique has been evaluated on a large data set of four main types namely: mold, intra-oral, extra-oral and radiographic images of different patients. Hence, experimental results demonstrate the good performances of this approach.
Bulletin of Electrical Engineering and Informatics, 2021
This study proposes a new contribution to solve the problem of automatic landmarks detection in t... more This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. Th...
International Journal of Electrical and Computer Engineering (IJECE), 2020
The purpose of this study is to investigate computer vision and machine learning methods for clas... more 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%.
Advances in Intelligent Systems and Computing, 2020
International Journal of Electrical and Computer Engineering (IJECE), 2020
The purpose of this study is to investigate computer vision and machine learning methods for clas... more 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), Euclid...
International Orthodontics, 2018
Objective: The aim of this study was to develop a new method for an automatic detection of refere... more Objective: The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. Materials and methods: A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. Results: The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32 mm. Discussion: In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric
International Orthodontics, 2018
2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), 2016
Recognition and classification of images have a wide field of applications, especially in medical... more Recognition and classification of images have a wide field of applications, especially in medical images. In order to provide orthodontists a solution for classification of patients' images to evaluate the evolution of their treatment, we need to use latest efficient technics of classification. In this paper, we propose an algorithm based on a decisional tree to classify and recognize 19 types of dental images. This hierarchical representation can be interpreted as a set of hierarchical types stored in leafs tree structure. By using several extracted features from color images acquired with a digital camera and grayscale images acquired by x-ray scanner. Such as facial features and skin color using YCbCr color-space. The proposed technique has been evaluated on a large data set of four main types namely: mold, intra-oral, extra-oral and radiographic images of different patients. Hence, experimental results demonstrate the good performances of this approach.
Bulletin of Electrical Engineering and Informatics, 2021
This study proposes a new contribution to solve the problem of automatic landmarks detection in t... more This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. Th...
International Journal of Electrical and Computer Engineering (IJECE), 2020
The purpose of this study is to investigate computer vision and machine learning methods for clas... more 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%.