Age-Related Macular Degeneration and Its Risk Factors in North Africans Living in Algeria and Italy.

PURPOSE: To determine the risk factors for age-related macular degeneration (AMD) in Algerians, and compare these data with those on North Africans living in Italy. METHODS: All patients over 55 years of age consulting one of the 23 involved Algerian ophthalmologists were invited to participate, and 1,183 patients were included. Data collection was standardized based on the Simplified Théa Risk Assessment Scale (STARS) questionnaire. A similar study was conducted in North Africans living in Italy (n = 1,011). Patients with only soft drusen and/or pigmentary abnormalities were classified as early AMD, and patients with geographic atrophy and/or neovascular AMD were classified as late AMD. RESULTS: In the final multivariate model, risk for early and/or late AMD was significantly increased with older age, family history of AMD, Black ethnicity, atherosclerosis, beer consumption, high fruit consumption, cataract surgery, myopia, and hyperopia. High consumption of green vegetables was associated with lower risk for both early and late AMD. In comparison with North Africans from Italy, Algerians generally had a healthier profile (younger, less obesity, smoking, and cardiovascular diseases, and higher consumption of fruits and vegetables) and a lower risk for AMD. CONCLUSION: This study documents risk factors for AMD in North-African populations for the first time.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation.

In this paper, a new image segmentation method based on Particle Swarm Optimization (PSO) and outlier rejection combined with level set is proposed. A traditional approach to the segmentation of Magnetic Resonance (MR) images is the Fuzzy C-Means (FCM) clustering algorithm. The membership function of this conventional algorithm is sensitive to the outlier and does not integrate the spatial information in the image. The algorithm is very sensitive to noise and in-homogeneities in the image, moreover, it depends on cluster centers initialization. To improve the outlier rejection and to reduce the noise sensitivity of conventional FCM clustering algorithm, a novel extended FCM algorithm for image segmentation is presented. In general, in the FCM algorithm the initial cluster centers are chosen randomly, with the help of PSO algorithm the clusters centers are chosen optimally. Our algorithm takes also into consideration the spatial neighborhood information. These a priori are used in the cost function to be optimized. For MR images, the resulting fuzzy clustering is used to set the initial level set contour. The results confirm the effectiveness of the proposed algorithm.