تحديات العولمة للشباب ومواجهتها في ضوء السيرة النبوية

Challenges are part of the ways of life, and the Islamic world is suffering of the rupture and differentiates internal conflicts, and the chaos of thought and delayed civilization. The most prominent of these challenges, which the Islamic world is facing, is the phenomenon of globalization. Which included the three worlds, and sought to dominate them, and melt the people’s properties and this is what precipitated the emergence of numerous drawbacks, and the researcher specially mentioned, negative aspects of identity, ethics and the environment. Young people who are the hope and asset of this nation are able to turn dreams into realities, and hopes to practical projects, and ambitions to the visible achievements. They are worthwhile, and the most capable and powerful to meet these challenges. If they grasp the necessary requirements that qualify and stables them to face it and take advantage of its goodness, and avoid disadvantage. But if they stationed, and failed to do their duty, they offered loss and destruction to themselves and nation. Curriculum should be prepared in accordance with the system of Islamic values, which are the individual configuration integrated, in belief, thought, and behavior, and attention to the development of the curriculum, and make it combines tradition and modernity, and the distinction between fixed and variable, and have superior mental abilities. All these issues are addressed in this research the negative impact of globalization on the environment, ethics and then showed the importance of taking care of the human development of young people, through various training programs that earn young people skills, science, and sophisticated learning methodical thinking, proper planning, and positive participation in society, and carry responsibility, and foster in them the ability to dialogue and to develop in them the spirit of creativity and scientific research and rational dealing with crises, and tribulations, and the ability to solve problems and meet the challenges of globalizatio

A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis

Automated vertebrae analysis from medical images plays an important role in computer aided diagnosis (CAD). It provides an initial and early identification of various vertebral abnormalities to doctors and radiologists. Vertebrae segmentation and classification are important but difficult tasks in medical imaging due to low contrasts in image, noise and high topological shape variations in radiological scans. It becomes even more challenging when dealing with various deformities and pathologies present in the vertebral scans like osteoporotic vertebral fractures. In this work, we want to address the challenging problem of vertebral image analysis for vertebra segmentation and classification. In the past, various traditional imagery techniques were employed to address these problems. Recently, deep learning techniques have been introduced in biomedical image processing for segmentation and characterization of several abnormalities. These techniques are becoming popular in solving various medical image analysis problems due to their robustness and accuracy. In this research, we present a solution of vertebrae segmentation and classification problem with the help of deep learning approach. We present a novel combination of traditional region based level-set with deep learning framework in order to extract the shape of vertebral bones accurately; which would be able to handle the deformities in the vertebral bones precisely and efficiently. After vertebrae segmentation, we further extend the work to abnormal vertebrae classification with the help of deep learning approach. This novel framework would be able to help the medical doctors and radiologists with better visualization of vertebral bones and providing the initial automated classification of vertebrae to be normal or abnormal. The proposed method of vertebrae segmentation was successfully tested on different datasets with various fields of views. The first dataset comprises of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view, collected from a local hospital. The second dataset comprises 73 CT scans of cervical vertebrae in sagittal view, also collected from a local hospital. The third dataset comprises 20 CT scans of thoracolumbar (thoracic and lumbar) vertebrae in sagittal view collected from spine segmentation challenge CSI 2014. The forth dataset comprises 25 CT scans of lumbar vertebrae in sagittal view collected from spine segmentation challenge CSI 2016. Lastly, we have utilized the same locally collected set of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae and extracted in axial view to perform the segmentation task.For classification purpose, we have utilized the locally collected set of 173 CT scans of thoracolumbar (thoracic and lumbar) vertebrae as these include osteoporotic vertebral fractures in it. The details of these datasets have been presented in respective sections. We have achieved promising results on our proposed techniques. The evaluation of the segmentation performance on the datasets with various machines and field of views helped us to ensure the robustness of our proposed method. On validation sets of these datasets, we have achieved an average dice score of around 95% for vertebrae segmentation; and accuracy of above 80% for the vertebrae classification. The detailed results have been presented in the results section. These results reveal that our proposed techniques are competitive over the other state of the arts in terms of accuracy, efficiency, flexibility and time.