| Summary: | 碩士 === 國立中山大學 === 機械與機電工程學系研究所 === 104 === With the rapid development of medical quantification, image analysis techniques were gradually applied to the research of clinical medicine. For example, Magnetic Resonance Imaging (MRI) is one of the image technologies that is widely used for medical imaging. If we can use appropriate image processing algorithms to analyze the MRI images, the produced results will be more objective than conventional visual evaluation. In addition, they can assist doctors to discriminate the relationship between the changes of brain’s substances and the lesions so as to predict patients’ condition.
This thesis aims to develop an algorithm that can effectively detect the White Matter Hyperintensities (WMHs) in brain MRI image. WMHs is an important basis that may cause potential diseases on the brain’s upper half in brain medicine. Therefore, if we can detect its area, it must be great aid when doctors make diagnosis. In algorithm design, we treat MRI as a kind of multispectral imaging. First, we use Band Expansion Process (BEP) to increase the amount of band images, and use Automatic Target Generation Process (ATGP) to detect the initial location of WMHs. Second, we propose an algorithm, called Iterative Constrained Energy Minimization (ICEM), which can detect the WMHs’ distribution and suppress the surrounding non-lesion organizations simultaneously.
Except for the visual results, we also design a quantification method for the experiment. We set different thresholds to convert the ICEM’s maps to binary images, then use Similarity Index (SI) to evaluate the accuracy of lesions’ examination. By virtue of the experiments conducted on both clinical and synthetic MRI image data, our algorithm can effectively find the WMHs’ distribution. Besides, through the extra experiment on progressive analysis, we find that using the BEP bands produced by T1-weighted image can significantly increase the performance of WMH’s detection.
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