| Summary: | 碩士 === 國立嘉義大學 === 資訊管理學系研究所 === 102 === Multiple sclerosis is a disease in the central nervous system including the brain and spinal cord. Patients' nerve has many demyelination damaged locations, so called multiple. Patients will appear mobility, impaired vision, pain and other symptoms. Neurologists often use magnetic resonance imaging (MRI) technology to diagnose this disease. Doctors can identify the location of the occurrence of multiple sclerosis lesions by MRI. There are usually only a small number of pixels on the image belonging to MS lesions, so the image recognition is not easy for human judgment. In this study, we applied statistical models with pixel intensity, location, and neighborhood information to find the location of MS on MRI. The methods we used are conditional random fields, Markov random fields, Naïve Bayes classifier. We integrated these three methods to increase precise rate in finding MS lesions. Three kinds images including T1, T2 and FLAIR are cross combined for performance evaluation. Experiment results show that combining T2 and FLAIR images has the best performance. The proposed method has more accurate to find out the MS lesions. The results show the proposed method can identify MS lesions effectively and can reduce time consumed in image recognition for doctors.
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