本文選題：阿爾茨海默病 + 輕度認知障礙　； 參考：《山東師范大學(xué)》2017年碩士論文

【摘要】：阿爾茨海默病(Alzheimer’s disease,AD),是一種最常見的神經(jīng)系統(tǒng)退行性疾病。隨著人口老齡化進程的加劇,阿爾茨海默病患者逐年大量增加,造成了十分巨大的社會影響。然而由于阿爾茨海默病的病變原因比較復(fù)雜,而且科學(xué)研究和臨床檢查沒有足夠的特異性,使得對該病早期的診斷準確率偏低。阿爾茨海默病的臨床檢查發(fā)展過程比較緩慢,能夠有效進行早期無創(chuàng)敏感的診斷方法并不多。隨著計算機圖像處理方法和磁共振成像技術(shù)的發(fā)展,使用磁共振成像技術(shù)進行早期無創(chuàng)的體外檢測顱內(nèi)結(jié)構(gòu)發(fā)展為一種有效可靠的方法。在臨床上,磁共振成像對阿爾茨海默病的診斷和治療有著非常重要的意義。本文利用磁共振成像數(shù)據(jù),對阿爾茨海默病患病人群、輕度認知障礙人群以及正常老年人的大腦皮層的形態(tài)學(xué)進行了比較深入的研究。本論文研究內(nèi)容和成果主要包括以下幾個方面:首先,本文通過研究分析相關(guān)特征選擇算法,得出一種新的特征選擇算法,通過排名融合規(guī)則,將m RMR和Relief相結(jié)合,充分利用考慮Filter類特征選擇方法的較高的計算效率以及利用Wrapper類特征選擇方法使用分類效果作為評價標(biāo)準具有較好的分類精度,使用新的特征選擇方法對經(jīng)過圖像預(yù)處理的正常老年人和輕度認知障礙人群的磁共振數(shù)據(jù)進行特征選擇,使得新的特征選擇算法選出的特征子集具有較高的分類準確率。其次,為了使分類效果更優(yōu)以及降低對融合過程的權(quán)重選擇的時間復(fù)雜度,提出使用粒子群算法進行權(quán)重尋優(yōu),可以更快得到更好的權(quán)重系數(shù),使得新的特征選擇算法系統(tǒng)具有較低的時間復(fù)雜度。選出的特征對分類器具有更好的效果,并對算法進行了橫向比較,在常用相關(guān)特征選擇算法中具有比較明顯的優(yōu)勢。最后,使用新的特征選擇算法結(jié)合支持向量機進行特征選擇及特征分類研究,選出具有最佳分類效果的特征并對比腦區(qū)圖像,驗證選出的特征對應(yīng)的腦區(qū)部位主要與海馬旁回相關(guān),與前人的相關(guān)研究有一定相似性,同時也發(fā)現(xiàn)新的不相同的腦區(qū)左側(cè)枕上和右側(cè)楔前葉,為將來的研究提供了一定參考。
[Abstract]:Alzheimer's disease (AD) is one of the most common neurodegenerative diseases.With the aggravation of population aging, Alzheimer's disease patients are increasing year by year, resulting in great social impact.However, the early diagnostic accuracy of Alzheimer's disease is low due to the complexity of the disease and the lack of specificity in scientific research and clinical examination.The clinical examination of Alzheimer's disease develops slowly and there are few effective methods for early noninvasive and sensitive diagnosis.With the development of computer image processing and magnetic resonance imaging, it is an effective and reliable method to use magnetic resonance imaging to detect intracranial structures in vitro.Magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of Alzheimer's disease.The morphology of cerebral cortex in patients with Alzheimer's disease, mild cognitive impairment and normal elderly was studied by magnetic resonance imaging (MRI).The main contents and achievements of this paper include the following aspects: firstly, by analyzing the relevant feature selection algorithms, a new feature selection algorithm is proposed, which combines m RMR and Relief by ranking fusion rules.Taking full advantage of the high computational efficiency of considering the Filter class feature selection method and using the Wrapper class feature selection method to use the classification effect as the evaluation criterion, it has good classification accuracy.The new feature selection method is used to select the magnetic resonance (MRI) data of the normal elderly and mild cognitive impairment population after image preprocessing, which makes the feature subset selected by the new feature selection algorithm have a higher classification accuracy.Secondly, in order to make the classification effect better and reduce the time complexity of weight selection in the fusion process, a particle swarm optimization algorithm is proposed for weight optimization, which can get better weight coefficients faster.The new feature selection algorithm system has lower time complexity.The selected features have a better effect on the classifier, and the algorithm is compared horizontally, which has obvious advantages in the common related feature selection algorithms.Finally, a new feature selection algorithm combined with support vector machine (SVM) is used to study the feature selection and feature classification. The features with the best classification effect are selected and compared with the brain image.The selected regions were mainly related to the parahippocampal gyrus and were similar to previous studies. At the same time, some new different regions of the brain were found in the left superior occipital area and the right anterior cuneate lobe, which provided a certain reference for future research.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R749.16;TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 李昕;陳澤濤;謝佳利;楊亞丹;;阿爾茨海默癥早期評估研究綜述[J];生物醫(yī)學(xué)工程學(xué)雜志;2015年05期

2 李玲;劉華文;徐曉丹;趙建民;;基于信息增益的多標(biāo)簽特征選擇算法[J];計算機科學(xué);2015年07期

3 呂艷陽;相潔;;基于SVM的fMRI數(shù)據(jù)分類及MCI診斷應(yīng)用[J];計算機工程與設(shè)計;2013年09期

4 林建海;陸開;孫雨軒;;基于均值-方差模型的Relief特征選擇優(yōu)化算法[J];系統(tǒng)仿真技術(shù);2013年03期

5 劉蘇;楊慧芳;劉衛(wèi)芳;張景;夏，

本文編號：1750616