碩士 / 中原大學 / 生物醫學工程研究所 / 106 / Due to advances in medical technology, the average life expectancy of human beings has been prolonged and the rupture rate of diseases caused by aging was increase as well. Alzheimer''s disease is caused by the deterioration of the brain nerves and need to be detected as early as possible. Currently, clinical diagnosis of Alzheimer''s disease is mainly based on a more subjective scale, and there is no exact quantitative standard. It is difficult to accurately diagnose this disease at the early stage. In this study, a system which could calculate of brain tissue atrophy parameters using image processing methods was developed, and could provide an objective reference for physician to make diagnosis in early stage.
This developed computer-aided detection system can calculates the thickness of brain gray matter and the volume ratio of brain parenchyma, specific sulcus, ventricles to skull through image processing methods to analysis of the severity of Alzheimer''s disease. Regional growth methods and filters were used to remove noise and segment the complete brain tissue. A threshold was set to distinguish brain tissue from cerebrospinal fluid, and then brain parenchymal ratio was calculated. The regional growth method was used to circle specific sulci and ventricles. Image enhancement technology was used to segment gray matter and then the morphological processing was used to obtain the graphical skeleton of the gray value region to calculate the gray matter thickness. Finally, the correlation between CDR and CDR scores which created with support vector machine (SVM) based on parameters of volume ratio of brain parenchyma, specific sulci, ventricle to skull and gray matter thickness was found. In this study, totally 60 sets of CT images were used, 40 sets of which were used as training groups and 20 sets were used as test groups, to train and test this system. System performance evaluation was performed by using the comparison with CDR and MRI images of patients.
In this system, SVM classifiers were trained in by 11 parameters which including parenchyma ratio, left and right brain parietal sulcus, left and right brain central sulcus, left and right lateral sulcus, cerebral ventricle, third ventricle, cerebral cistern, and whole ventricle in brain image. After using 20 sets of test groups to test the performance of the classifier, the accuracy, sensitivity, specificity, and Kappa value of the obtained classifier are 80%, 86.6%, 84.6%, and 0.547, respectively. The cases with significant difference in brain parenchymal volume ratio and CDR level in the training group were excluded; the statistical classifier performance of accuracy, sensitivity, specificity, and Kappa values are improved to 88%, 100%, 84.6% and 0.699, respectively.
The results show that the developed system can provide physicians with more objective quantitative data from CT images to assess the degrees of deterioration of Alzheimer''s disease, which has similar performance compared to mainstream MRI-based system. However, the system needs to manually select seed points when selecting the sulci and ventricles; this will result in errors and long interpretation time. In the future, an automatic identification system shall be developed to increase the efficiency of the system and the correct rate of selection.
| Identifer | oai:union.ndltd.org:TW/106CYCU5114020 |
| Date | January 2018 |
| Creators | Yao-Hsun Tien, 田耀勛 |
| Contributors | Jenn-Lung Su, 蘇振隆 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 96 |
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