Competitivite de la filiere huile d’olive en Algerie: Cas de la wilaya de bejaia

L'oléiculture algérienne a connu ces dernières décennies de profondes mutations, pour sa mise à niveau nécessaire à son intégration dans l’économie mondiale. En effet, la concurrence qui résulte de la libéralisation des échanges a incité les entreprises de ce secteur à améliorer leurs performances et leur compétitivité. Dans ce travail, nous nous proposons d'évaluer la position compétitive de la filière de l'huile d'olive en Algérie. Cette analyse porte d'une part sur la compétitivité, fondée sur le calcul des coûts de production dans les exploitations agricoles de la wilaya de Bejaia, principale zone de production oléicole en Algérie. D’autre part, elle aborde les aspects de compétitivité «hors-prix», déterminés par la qualité qui différencie les produits. L’Algérie ne dispose pas d’un avantage comparatif sur les prix ; mais paradoxalement ses traditions de consommation restent en faveur d’une qualité immatérielle qui exclut le goût algérien du standard international.Mots clés : Filière huile d’olive, Compétitivité, Bejaia, Algérie.Code Jel: Q1, R1

Analysis of scoliosis trunk deformities using ICA

This paper describes a method for analyzing scoliosis trunk deformities using Independent Component Analysis (ICA). Our hypothesis is that ICA can capture the scoliosis deformities visible on the trunk. Unlike Principal Component Analysis (PCA), ICA gives local shape variation and assumes that the data distribution is not normal. 3D torso images of 56 subjects including 28 patients with adolescent idiopathic scoliosis and 28 healthy subjects are analyzed using ICA. First, we remark that the independent components capture the local scoliosis deformities as the shoulder variation, the scapula asymmetry and the waist deformation. Second, we note that the different scoliosis curve types are characterized by different combinations of specific independent components.CIHR (Canadian Institutes of Health Research

Three-dimensional Segmentation of the Scoliotic Spine from MRI using Unsupervised Volume-based MR-CT Synthesis

Vertebral bone segmentation from magnetic resonance (MR) images is a challenging task. Due to the inherent nature of the modality to emphasize soft tissues of the body, common thresholding algorithms are ineffective in detecting bones in MR images. On the other hand, it is relatively easier to segment bones from CT images because of the high contrast between bones and the surrounding regions. For this reason, we perform a cross-modality synthesis between MR and CT domains for simple thresholding-based segmentation of the vertebral bones. However, this implicitly assumes the availability of paired MR-CT data, which is rare, especially in the case of scoliotic patients. In this paper, we present a completely unsupervised, fully three-dimensional (3D) cross-modality synthesis method for segmenting scoliotic spines. A 3D CycleGAN model is trained for an unpaired volume-to-volume translation across MR and CT domains. Then, the Otsu thresholding algorithm is applied to the synthesized CT volumes for easy segmentation of the vertebral bones. The resulting segmentation is used to reconstruct a 3D model of the spine. We validate our method on 28 scoliotic vertebrae in 3 patients by computing the point-to-surface mean distance between the landmark points for each vertebra obtained from pre-operative X-rays and the surface of the segmented vertebra. Our study results in a mean error of 3.41 $\pm$ 1.06 mm. Based on qualitative and quantitative results, we conclude that our method is able to obtain a good segmentation and 3D reconstruction of scoliotic spines, all after training from unpaired data in an unsupervised manner.Comment: To appear in the Proceedings of the SPIE Medical Imaging Conference 2021, San Diego, CA. 9 pages, 4 figures in tota

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.IRSC / CIH

Segmentation-Free Thinning and Enhancement of Grayscale Images by Shock Filter and Diffusion Fields

In the scope of gray-level image processing and understanding, thinning is certainly a central shape descriptor for image analysis and pattern recognition. Enhancement is also an essential tool in facilitating the visual interpretation and understanding of images, especially for noisy and blurry ones. The lack of general unified frameworks necessitates the investigation of these problems in a coherent fashion, using partial differential equations. In this paper, we present a method for thinning and enhancing images by using a shock filter derived from our previously work introduced on enhancement. This new filter incorporates specific diffusion fields and since each such field is characteristic of a given application, it brings a new degree of freedom to the shock filters, in order to address problems of greater practical interests. Probative results on handwritten documents illustrate the performance and efficiency of our model. Other applications have been added in order to highlight its efficiency.L’amincissement est assurément un descripteur de forme majeur pour l’analyse d’image et la reconnaissance de forme. Le rehaussement est aussi un outil essentiel pour faciliter l’interprétation visuelle et la compréhension des images de documents notamment celles qui sont bruitées et floues. Nous décrivons dans cet article une méthode d’amincissement et de rehaussement utilisant un filtre de chocs dérivant de celui introduit par Remaki et Cheriet pour le rehaussement. Ce nouveau filtre utilise un champ de diffusion spécifique initial. L’utilisation de tels champs apporte un nouveau degré de liberté aux filtres de chocs, puisque ceux-ci sont spécifiques aux applications (amincissement, rehaussement) et permettent ainsi au même filtre d’être utilisé pour différentes applications. Nous illustrons la performance de notre méthode par des résultats probants obtenus sur des images manuscrites

Scoliosis Follow-Up Using Noninvasive Trunk Surface Acquisition

Adolescent idiopathic scoliosis (AIS) is a musculoskeletal pathology. It is a complex spinal curvature in a 3-D space that also affects the appearance of the trunk. The clinical follow-up of AIS is decisive for its management. Currently, the Cobb angle, which is measured from full spine radiography, is the most common indicator of the scoliosis progression. However, cumulative exposure to X-rays radiation increases the risk for certain cancers. Thus, a noninvasive method for the identification of the scoliosis progression from trunk shape analysis would be helpful. In this study, a statistical model is built from a set of healthy subjects using independent component analysis and genetic algorithm. Based on this model, a representation of each scoliotic trunk from a set of AIS patients is computed and the difference between two successive acquisitions is used to determine if the scoliosis has progressed or not. This study was conducted on 58 subjects comprising 28 healthy subjects and 30 AIS patients who had trunk surface acquisitions in upright standing posture. The model detects 93% of the progressive cases and 80% of the nonprogressive cases. Thus, the rate of false negatives, representing the proportion of undetected progressions, is very low, only 7%. This study shows that it is possible to perform a scoliotic patient's follow-up using 3-D trunk image analysis, which is based on a noninvasive acquisition technique.IRSC / CIH