碩士 / 國立雲林科技大學 / 工業工程與管理系 / 105 / Alzheimer's disease mainly occurs in the elderly over the age of 65. Since the global aging of population is increasingly serious, there is a trend that the dementia patients growing faster and faster. It is worth mentioning that the recent research points out there is highly correlated between Alzheimer and dementia.
Both Alzheimer and dementia is progressive degenerative brain disease.
At present, the diagnosis of the two diseases is mainly through the scale and the interrogation to make judgments. In addition, Alzheimer can also be diagnosed through the brain magnetic resonance imaging. Compared with Alzheimer's disease, dementia is currently no corresponding medical imaging diagnosis. Therefore, this study would like to expand the traditional measurement methods to figure out a set of objective and effective way to identify the dementia, hoping that we can find a decision that can provide doctors with a more discernible indicator of the diagnosis of Alzheimer's disease and dementia.
First of all, we will extract the brain characteristics of indicators from MRI and DTI. MRI images are divided into seven regions by block cutting.
And then we will use the FA value which we get from the DTI image to do the skeletonization.
Our methods are skeletonization images segmentation of brain regions, the left half, right half, the integrity of the white matter of the whole brain seven regions of the brain (Frontal、Temporal、Parietal 、Occipital、Limbic、midbrain、Sub lobar), and the use of diffusion tensor imaging in λ1, λ2, λ3, FA(Fractional Anisotropy), MD(Mean Diffusivity), Cl(Linear), Cp(Planar), Cs(Spherical), RD(Radial Diffusivity) quantitative information to feature selection.
After GRNN and Logistic regression analysis performance measures for the receiver operating characteristic decision tree model, the probability of drawing each node curve.
We hoping to find a set of objective rules of functional information, to provide physician as a secondary diagnostic purposes.
| Identifer | oai:union.ndltd.org:TW/105YUNT0031046 |
| Date | January 2017 |
| Creators | JHENG, YI-TING, 鄭亦廷 |
| Contributors | FU, JA-CHIH, 傅家啟 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 84 |
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