在辨識輕度認知障礙與阿茲海默症中，由於面臨缺乏非侵入式且有效的檢驗方法的情況下，此論文致力於提出在磁核照影像中提取出俱科學量化的生物特徵來辨識輕度認知障礙與阿茲海默症，論文中提出兩種分析演算法，第一個演算法將會先在海馬迴子區中萃取特徵如體積、表面積與曲率來模擬海馬迴結構上的變化，例如萎縮和顯示出大腦皮質生物特徵-皺褶，此外此論文更提出一種創新的生物特徵，為主曲率比率，用來萃取海馬迴表面的皺褶與大腦溝或是大腦迴的相似程度，這些結構形態上的特徵與腦神經生物相關的特徵將於論文中使T測試來進行假說測試，論文中的假說假設當病人由輕度認知障礙轉變成阿茲海默症的情況發生時，腦內海馬迴所提取的曲率將會增加，此外這些特徵將被輸入於多層神經感測器來分類阿茲海默症之嚴重程度。從第一個演算法的結論中，得知當病人由輕度認知障礙轉變成阿茲海默症的情況發生時，體積、表面積與曲率在前下部支持組織(presubiculum)、下部支持組織(subiculum)與CA1、CA3的子區內顯示具有統計意義上的變化，但是當診斷保持在輕度認知障礙時沒有顯著變化。在一系列的特徵選取後，多層神經感測器在分類輕度認知障礙的病人是否轉變成阿茲海默症的實驗中得到77.3%分類準確度。在第二個分析演算法中，則是採用了深度學習演算法中的三維捲積神經網路來分類輕度認知障礙與阿茲海默症，捲積神經網路具有影像處理技術來萃取特徵進行分類，此方法幫助我們測試使用捲積神經網路來分類輕度認知障礙與阿茲海默症的可能性。

Owing to the lack of non-invasive and efficient examination and method to identify Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), this thesis is dedicated to providing scientific and precise quantitative biomarkers from Magnetic Resonance Imaging (MRI) for identifying the conversion from MCI to AD. Therefore, we established two proposed analytic algorithms. The first proposed algorithm will extract three features – volume, surface area and average of curvature within the hippocampal subfields to model variations including atrophy and the description about the neurobiological characteristics on cortical surface – the folds. Furthermore, we also proposed an innovative biomarker – ratio of principal curvatures for measuring the similarity to gyrus and sulcus. The morphological and neurobiology-related features will be tested by T-test, and the hypothesis about the curvature increasing as MCI to AD will be tested as well. In addition, the extracted features are fed into Multilayer Perceptron classifier for classifying the diagnosis conversion (MCI converter and MCI-to-AD). We found that volume, surface area and average of curvature showed with statistical-meaning changes within presubiculum, subiculum, CA1 and CA3 when diagnosis of patients converted from MCI to AD but smaller variations in the MCI non-converter group. After a series of feature selection and the selection on the subfields of hippocampus, we achieved 77.3% classification accuracy of discrimination of MCI-to-AD group and MCI non-converter group with surface area and average of RPC extracted from subiculum, CA1 and CA3 as features fed into MLP. In the second proposed algorithm, we also applied a deep learning algorithm – 3D Convolutional Neural Network which equipped with image processing skills feature extractor to classify MCI and AD. This proposed algorithm helps us explore the potential to extract multiscale features for CNN according to the experimental results.

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