Publications Internationales
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Item Cancellous bone structure assessment using a new trabecular connectivity(Elsevier, 2021) Harrar, KhaledOsteoporosis is a major public health problem; it is characterized by a loss in bone connectivity, which leads to a higher risk of fracture. The objective of this article is to develop a new connectivity parameter for bone microarchitecture characterization and osteoporosis assessment. The purpose is to discriminate 164 subjects composed of 82 healthy patients (HL) and 82 osteoporotic cases (OP). The new connectivity parameter involves several new topological features. The proposed method was compared to a traditional connectivity index, and the results reveal the superiority and the outperformance of the new parameter to discriminate the two groups of subjects with an accuracy (Acc) of 71.95 % and area under curve (AUC) of 80.03 %. Moreover, clinical parameters from patients were involved in this study, and five configurations were constructed, tested, and validated on the data using the k-fold cross-validation (CV) model with several values of k. Furthermore, support vector machine (SVM) was used and various kernels (i.e., linear, quadratic, cubic, and RBF functions) were tested in this study. The objective is to look for the configuration providing the best performance in terms of separation between the two populations. Furthermore, several classifiers (logistic regression, k-nearest neighbors, boosted trees, and naïve Bayes) were tested and a combination of these classifiers was carried out using the stacking ensemble technique to improve the accuracy of the final prediction. Moreover, several studies of state-of-the-art were compared to the proposed method. The results obtained reveal that the 10-fold CV approach combining the new trabecular connectivity index and RBF function of SVM achieved the highest accuracy with Acc = 88.41 %, and AUC = 95.24 %. In addition, the proposed ensemble Meta classifier improved the accuracy of SVM and achieved a high rate with Acc = 95.12 % and AUC = 98.40 % outperforming the existing methods in the literatureItem Oriented fractal analysis for improved bone microarchitecture characterization(Elsevier, 2017) Harrar, Khaled; Jennane, Rachid; Zaouchi, Karima; Janvier, Thomas; Toumi, Hechmi; Lespessailles, EricItem Osteoporosis assessment using Multilayer Perceptron neural networks(IEEE, 2012) Harrar, Khaled; Hamami, L.; Akkoul, S.; Lespessailles, E.The objective of this paper is to investigate the effectiveness of a Multilayer Perceptron (MLP) to discriminate subjects with and without osteoporosis using a set of five parameters characterizing the quality of the bone structure. These parameters include Age, Bone mineral content (BMC), Bone mineral density (BMD), fractal Hurst exponent (Hmean) and coocurrence texture feature (CoEn). The purpose of the study is to detect the potential usefulness of the combination of different features to increase the classification rate of 2 populations composed of osteporotic patients and control subjects. k-fold Cross Validation (CV) was used in order to assess the accuracy and reliability of the neural network validation. Compared to other methods MLP-based analysis provides an accurate and reliable platform for osteoporosis prediction. Moreover, the results show that the combination of the five features provides better performance in terms of discrimination of the subjectsItem Trabecular Texture Analysis Using Fractal Metrics for Bone Fragility Assessment(World Academy of Science, Engineering and Technology, 2015) Harrar, Khaled; Jennane, RachidThe purpose of this study is the discrimination of 28 postmenopausal with osteoporotic femoral fractures from an age-matched control group of 28 women using texture analysis based on fractals. Two pre-processing approaches are applied on radiographic images; these techniques are compared to highlight the choice of the pre-processing method. Furthermore, the values of the fractal dimension are compared to those of the fractal signature in terms of the classification of the two populations. In a second analysis, the BMD measure at proximal femur was compared to the fractal analysis, the latter, which is a non-invasive technique, allowed a better discrimination; the results confirm that the fractal analysis of texture on calcaneus radiographs is able to discriminate osteoporotic patients with femoral fracture from controls. This discrimination was efficient compared to that obtained by BMD alone. It was also present in comparing subgroups with overlapping values of BMDItem Piecewise whittle estimator for trabecular bone radiograph characterization(Elsevier, 2013) Harrar, Khaled; Hamami, Latifa; Lespessailles, Eric; Jennane, RachidItem An interconnectivity index for osteoporosis assessment using X-Ray images(2013) Harrar, Khaled; Hamami, L.