Color edges extraction using statistical features and automatic threshold technique: application to the breast cancer cells

BACKGROUND: Color image segmentation has been so far applied in many areas; hence, recently many different techniques have been developed and proposed. In the medical imaging area, the image segmentation may be helpful to provide assistance to doctor in order to follow-up the disease of a certain patient from the breast cancer processed images. The main objective of this work is to rebuild and also to enhance each cell from the three component images provided by an input image. Indeed, from an initial segmentation obtained using the statistical features and histogram threshold techniques, the resulting segmentation may represent accurately the non complete and pasted cells and enhance them. This allows real help to doctors, and consequently, these cells become clear and easy to be counted. METHODS: A novel method for color edges extraction based on statistical features and automatic threshold is presented. The traditional edge detector, based on the first and the second order neighborhood, describing the relationship between the current pixel and its neighbors, is extended to the statistical domain. Hence, color edges in an image are obtained by combining the statistical features and the automatic threshold techniques. Finally, on the obtained color edges with specific primitive color, a combination rule is used to integrate the edge results over the three color components. RESULTS: Breast cancer cell images were used to evaluate the performance of the proposed method both quantitatively and qualitatively. Hence, a visual and a numerical assessment based on the probability of correct classification (P( C )), the false classification (P( f )), and the classification accuracy (Sens(%)) are presented and compared with existing techniques. The proposed method shows its superiority in the detection of points which really belong to the cells, and also the facility of counting the number of the processed cells. CONCLUSIONS: Computer simulations highlight that the proposed method substantially enhances the segmented image with smaller error rates better than other existing algorithms under the same settings (patterns and parameters). Moreover, it provides high classification accuracy, reaching the rate of 97.94%. Additionally, the segmentation method may be extended to other medical imaging types having similar properties

New Hopfield Neural Network for joint Job Shop Scheduling of production and maintenance.

International audienceJob Shop Scheduling is one of the most difficult problems in industry and it is the main interest of the major researchers in the manufacturing research area. This problem becomes crucial when the production planning and maintenance have to be jointly solved. Several heuristics and intelligent methods have been so far proposed in the literature and applied. This work deals with a Hopfield Neural Network (HNN) method used for solving the JSP taking into account the maintenance tasks. While this method had been already proposed in the literature to solve the JSP alone, our main improvement of this method is to take into account the maintenance periods by extending the Hopfield net to handle the joint problem. Experimental study shows that the proposed HNN algorithm gives efficient results for the resolution of the joint job shop scheduling problem

PLANIFICATION DES LIVRAISON JOINTES DE DIFFERENTS PRODUITS A DIFFERENTS SITES

Colloque avec actes et comité de lecture. internationale.International audienceLe problème de livraisons jointes de produits (JDP) consiste à planifier les livraisons de différents produits à différents sites de consommation ou de distribution en traitant les problèmes de groupement, de livraison et de stockage. Il s’agit de construire des tournées de livraison sur un horizon de planification, en satisfaisant les demandes et en minimisant le coût total de commande, de livraison et de stockage. Les coûts fixes de commande portent d’une part sur le lancement d’une tournée, d’autre part sur chaque couple (produit, site) présent ou non dans la tournée. Les taux de demandes étant supposées fixes et connus, le problème en horizon infini admet une solution périodique, le plan de livraison optimal sur une période-type pouvant se répéter indéfiniment. Dans notre approche, le problème est formulé en temps discret et nous choisissons comme période-type commune de cyclicité un multiple de la période élémentaire, et cette période-type sert d’horizon de planification. Ainsi, les livraisons restent périodiques à travers la répétition de l’horizon de planification, mais les livraisons pendant l’horizon de planification ne sont pas contraintes à être périodiques. Les résultats numériques montrent en particulier la supériorité de cette approche sur une solution cyclique pour chaque couple (produit, site)

Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Linear feature selection and classification using PNN and SFAM neural networks for a nearly online diagnosis of bearing naturally progressing degradations.

International audienceIn this work, an effort is made to characterize seven bearing states depending on the energy entropy of Intrinsic Mode Functions (IMFs) resulted from the Empirical Modes Decomposition (EMD).Three run-to-failure bearing vibration signals representing different defects either degraded or different failing components (roller, inner race and outer race) with healthy state lead to seven bearing states under study. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used for feature reduction. Then, six classification scenarios are processed via a Probabilistic Neural Network (PNN) and a Simplified Fuzzy Adaptive resonance theory Map (SFAM) neural network. In other words, the three extracted feature data bases (EMD, PCA and LDA features) are processed firstly with SFAM and secondly with a combination of PNN-SFAM. The computation of classification accuracy and scattering criterion for each scenario shows that the EMD-LDA-PNN-SFAM combination is the suitable strategy for online bearing fault diagnosis. The proposed methodology reveals better generalization capability compared to previous works and it’s validated by an online bearing fault diagnosis. The proposed strategy can be applied for the decision making of several assets

Caractérisation des textures avec les coefficients 2-D transverses et de réflexion : Une étude comparative

Dans cet article, on traite le problème de la caractérisation des textures avec de nouvelles approches de modélisation paramétrique. On se propose de fournir une réponse à la question suivante : lesquels parmi les coefficients 2-D transverses ou de réflexion 2-D ( treillis) permettent-ils de mieux caractériser les textures ? Pour ceci, on considère plusieurs classes de textures et on estime pour chaque texture les deux types de coefficients avec l'algorithme adaptatif 2-D FLRLS (2-D Fast Lattice Recursive Least Square). Comme critère de comparaison, on définit un pouvoir séparateur (rapport des variances entre-classes et dans la classe) pour chaque coefficients. On montre que les coefficients de réflexion présentent un meilleur pouvoir séparateur que celui des coefficients transverses

Is type 1 diabetes a chaotic phenomenon?

A database of ten type 1 diabetes patients wearing a continuous glucose monitoring device has enabled to record their blood glucose continuous variations every minute all day long during fourteen consecutive days. These recordings represent, for each patient, a time series consisting of 1 value of glycaemia per minute during 24 hours and 14 days, i.e., 20,160 data point. Thus, while using numerical methods, these time series have been anonymously analyzed. Nevertheless, because of the stochastic inputs induced by daily activities of any human being, it has not been possible to discriminate chaos from noise. So, we have decided to keep only the 14 nights of these ten patients. Then, the determination of the time delay and embedding dimension according to the delay coordinate embedding method has allowed us to estimate for each patient the correlation dimension and the maximal Lyapunov exponent. This has led us to show that type 1 diabetes could indeed be a chaotic phenomenon. Once this result has been confirmed by the determinism test, we have computed the Lyapunov time and found that the limit of predictability of this phenomenon is nearly equal to half the 90-minutes sleep-dream cycle. We hope that our results will prove to be useful to characterize and predict blood glucose variations

Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)

This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming