Global Structural Changes and Global Islamic Identity

Globalization is slowly changing life and traditions of many people over the World, dramatically seeking changes in the traditional relationship between the community and people, creating a new sensibility and creativity in relationships between social groups. These changes necessarily require a new social and political model of organization for community, reorganizing and changing the nature of relationship between states.  Effort to protect identity of people usually convey in the form of the fear of the subservient economic, cultural and political position in the process of globalization. This fear frequently produces powerful vibrations indicating the need of integration of social groups with the same or similar cultural identity, what opens up a new dimension of the internal political crisis between government and society. This crisis will produce particularly dramatic changes in Islamic world generating a powerful conflict between state and society in Islamic world, with unpredictable development of relations between Islam and West.

Retinal Image Analysis & Hybrid Classifier for Screening of Diabetic Retinopathy

Medical image analysis is very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated in time. Healthy retina contains blood vessels, optic disc and macula as main components but abnormal retina may contain other components and signs as well. An au- tomated system for early detection of DR can save patient’s vision and can also help the ophthalmologists in screening of DR. In this thesis, we develop algorithms for retinal image analysis based on image processing and pattern classification. Image processing techniques are used for retinal image enhancement and pattern recognition is used for classification of DR stages. The proposed system consists of different stages such as preprocessing, compo- nent extraction, candidate region detection, feature extraction and finally the classification. The first phase consists of input retinal image enhancement, noise removal, extraction of main retinal components and candidate lesions detection. We apply Gabor wavelets and Gabor filter banks for lesion detection. The system then extracts features from candidate lesions using four main properties, i.e. shape, color, gray level and statistical. Finally the classifier takes the feature vectors as inputs and grades the input retinal image into dif- ferent stages of DR. We present a hybrid classifier which combines the Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The im- plemented algorithms are tested and evaluated on publicly available retinal image databases using performance parameters such as sensitivity, specificity, positive predictive value and accuracy. The performance improvement of our proposed system is demonstrated by com- paring them with recently proposed and published methods.