A soft computing approach for enhancing fractional edge detection / Wessam Sayed Mohamed Sayed Elaraby ; Supervised Ibrahim Farag , Mahmoud Aly Ashour , Mohammad Nassef

Thesis (Ph.D.) - Cairo University - Faculty of Computers and Artificial Intelligence - Department of Computer Science

Medical Imaging plays a vital role in the researches and diagnosis of a lot of diseases, over the past five decades. Medical images are mostly used as radiographic techniques in clinical studies, diagnosis and treatment. Edge detection techniques could be helped in the diagnosis of early stages of diseases like Alzheimer and fracture bone. Edge detection is a vital scope in many applications in the image processing field. Edge detection makes use of integer-order differential methods to enhance the edge information effectively, but at the same time, it is easy to lose image detail information and can be sensitive to noise. To solvei this problem, the edge detection methods, have been used the fractional-order derivative. A comparison to the traditional edge detection techniques, soft computing can transact with the uncertainty in image processing in a better way. This thesis targets to enhance the edge detection by using the fractional algorithms with the soft computing techniques. The work is splitted into two parts. Part one, for enhancing the performance the fractional edge detection by using fuzzy logic and getting the optimal thresholds for each image by using genetic algorithm. Part two, for getting the optimal fractional mask by using genetic algorithm and Fminsearch

Issued also as CD