| Summary: | This thesis addresses the problem of detecting a common parasitic micro laria that causes loaisis, a major disease problem in Central and Western Africa. The dose of medicine to be administered to the patient is proportional to the estimated number of micro lariae in the patient's body. Therefore, proper estimation of the number of micro lariae is the key for conducting the right procedure. The clinical examination is necessary to estimate the micro lariae density in a blood sample drawn from the patient. Thereafter, visual inspection of the sample is performed. The main challenge in this work is, however, the development of an automatic detection system of micro lariae in 2-D images. Such problem is new in the image processing literature, and the development of such system is very important for performing better diagnosis and treatment of this disease and other similar diseases. A comprehensive review of, both generic and thin, object detectors in 2-D images is presented. A very robust method for microscopy image illumination correction is proposed, and a new powerful descriptor, the Hessian-Polar Context (HPC), for micro lariae is also introduced. These are then combined in a micro lariae detection system, where a simple, yet e cient, hypotheses generator is also presented. Additionally, several methods and applications for di erent image modalities are proposed. These involve a method and an application for the analysis of rice panicle in 2-D images. Additionally, an e cient method for artifact suppression in X-ray image is also proposed. The proposed methods are compared to a set of state-of-the-art methods. Experimental results show that the developed methods are great contributions to the microscopy and X-ray imaging elds.
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