碩士 / 國立高雄第一科技大學 / 電腦與通訊工程所 / 98 / Alzheimer''s Disease is an age-related, non-reversible brain disorder that develops over a period of years. People who have lost brain cells in the hippocampus area of the brain are more likely to develop Alzheimer''s Disease. The traditional method needs an experienced physician to observe the Hippocampal atrophy, which is inefficient and will lead to misjudgment when the physician lacks enough clinical experience. To this end, this thesis proposes a wavelet-based dynamic boundary extension algorithm to provide an efficientcy of physicians in screening. The proposed system includes the hippocampus boundary detection and volume calculation. The hippocampus boundary detection is targeted on selecting the Area of Interest (AOI), which can reduce the computation time and information to avoid the process of the unwanted interference. Next, we use Histogram Equalization to enhance the image of AOI and wavelet transform to estimate the initial boundary of hippocampus. By using the proposed dynamic boundary extension algorithm, we reconstruct the edge of hippocampus and calculate the total pixels inside the contour as the hippocampus area. To obtain the hippocampal volume of patients, the volume calculation is done by accumulating each image within the same group in the hippocampus area.
The dataset of this paper includes 10 patients. Each one patient has 124 MRI images with different locations. Converting by the three-dimensional reconstruction, Each one patient has 512 sagittal plane images. The experimental analysis is conducted by using the 320 images, which are selected from the 5120 images. Experimental results show that the successful identification of the hippocampus up to 83% accuracy, while the accuracy of the system still have the advantages of stable and fast calculation.
| Identifer | oai:union.ndltd.org:TW/098NKIT5650026 |
| Date | January 2010 |
| Creators | Jia-Wei Chi, 祁家瑋 |
| Contributors | none, 汪桓生 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 64 |
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