Browsing by Author "Akkoul, S."

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    3D reconstruction method of the proximal femur and shape correction
    (IEEE, 2014) Akkoul, S.; Hafiane, A.; Leconge, R.; Harrar, Khaled
    The aim of this work is to present a 3D reconstruction method of the proximal femur shape using contours identification from pairs of 2D X-ray radiographs without any prior acknowledge. 3D personalized model was reconstructed following a processing chain of seven different steps. After localization of the 2D contours on the images and the matching points of these contours, a 3D contour is generated using an algorithm based on a mathematical model. Thus, with a reduced number of pairs of images, we reconstruct a 3D points cloud, which enables obtaining a closed 3D surface. The accuracy of our approach was evaluated by comparing the reconstruction result with the 3D CT-scan reconstruction of cadaveric proximal femur. The estimated error shows that it is possible to rebuild the proximal femur shape from a limited number of radiographs
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    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 subjects