本文選題：關(guān)節(jié)軟骨 + 骨關(guān)節(jié)炎��； 參考：《南京航空航天大學(xué)》2017年碩士論文

【摘要】：關(guān)節(jié)軟骨覆蓋于骨表面,是骨關(guān)節(jié)的重要組成部分之一,主要作用是在關(guān)節(jié)活動(dòng)中承受力學(xué)負(fù)荷,緩沖震動(dòng)以及減少摩擦。關(guān)節(jié)軟骨基質(zhì)的主要成分為膠原蛋白和蛋白多糖。年齡、肥胖、外傷等因素會(huì)造成關(guān)節(jié)軟骨的變性甚至損傷,進(jìn)一步發(fā)展可能會(huì)導(dǎo)致骨關(guān)節(jié)炎的發(fā)生。由于在骨關(guān)節(jié)炎早期,關(guān)節(jié)軟骨僅發(fā)生組分含量和結(jié)構(gòu)的變化,并不出現(xiàn)形態(tài)學(xué)上的改變。這使得目前常用的臨床診斷技術(shù)無(wú)法有效地識(shí)別早期骨關(guān)節(jié)炎。本文嘗試采用FTIRI技術(shù)結(jié)合不同的化學(xué)計(jì)量學(xué)識(shí)別算法對(duì)正常和病變關(guān)節(jié)軟骨進(jìn)行分類研究,尋找最優(yōu)的判別模型,期望為早期骨關(guān)節(jié)炎的準(zhǔn)確診斷開(kāi)辟新的途徑。其中,傅里葉變換紅光譜成像技術(shù)(FTIRI)可以同時(shí)獲得被測(cè)樣品的紅外光譜信息及其形貌特征,具備豐富的組分種類和含量信息�；瘜W(xué)計(jì)量學(xué)方法可以有效地提取光譜中的與相關(guān)化學(xué)組分對(duì)應(yīng)的特征信息,常用于光譜的定量和定性分析。其在物質(zhì)的定量分析和光譜分類識(shí)別等相關(guān)領(lǐng)域有著廣泛的應(yīng)用。本研究采集了關(guān)節(jié)軟骨的正常樣本、8周病變樣本以及2年病變樣本的光譜數(shù)據(jù),利用主成分分析(PCA)算法、Fisher判別(FDA)算法、偏最小二乘判別(PLS-DA)算法以及支持向量機(jī)判別(SVM-DA)算法分別構(gòu)建判別模型,實(shí)現(xiàn)對(duì)正常和病變光譜的分類識(shí)別。主要內(nèi)容為:(1)基于光譜預(yù)處理方法,利用PLS-DA算法對(duì)正常和2年病變組光譜進(jìn)行分類識(shí)別,預(yù)測(cè)準(zhǔn)確率為96.92%。(2)利用PCA結(jié)合FDA算法分別對(duì)未經(jīng)預(yù)處理的正常軟骨光譜和8周病變光譜以及2年病變光譜進(jìn)行分類識(shí)別。其中,正常vs 8周病變組的預(yù)測(cè)準(zhǔn)確率為89.23%,正常vs 2年病變組的預(yù)測(cè)準(zhǔn)確率為92.31%。(3)利用SVM-DA算法實(shí)現(xiàn)正常、8周病變和2年病變光譜的多類判別,整體預(yù)測(cè)準(zhǔn)確率為90.33%。當(dāng)利用SVM-DA實(shí)現(xiàn)正常vs 2年病變組光譜的二類判別時(shí),其預(yù)測(cè)準(zhǔn)確率為97.7%。比較3種模型的分類結(jié)果發(fā)現(xiàn),上述3種模型均可以有效地實(shí)現(xiàn)對(duì)正常和病變光譜的分類識(shí)別。其中,SVM-DA算法具有最佳的分類效果且可以有效地實(shí)現(xiàn)關(guān)節(jié)軟骨光譜的多類識(shí)別,有潛力發(fā)展成為一種新型的早期骨關(guān)節(jié)炎診斷方法,并為進(jìn)一步的研究提供理論依據(jù)和數(shù)據(jù)支持。
[Abstract]:Articular cartilage, which covers the surface of bone, is one of the important components of bone joint. The main function of articular cartilage is to withstand load, cushion vibration and reduce friction in joint motion.The main components of articular cartilage matrix are collagen and proteoglycan.Age, obesity, trauma and other factors may cause degeneration or injury of articular cartilage, and further development may lead to osteoarthritis.In the early stage of osteoarthritis, the articular cartilage changes only in composition and structure, but not in morphology.This makes the current commonly used clinical diagnosis technology can not effectively identify early osteoarthritis.This paper attempts to study the classification of normal and diseased articular cartilage by using FTIRI technique and different chemometrics recognition algorithms to find the best discriminant model and to open up a new way for the accurate diagnosis of early osteoarthritis.Fourier transform red spectral imaging (FTIRI) can simultaneously obtain the infrared spectrum information and its morphological characteristics of the samples, and it has abundant information on the composition and content of the samples.The chemometrics method can effectively extract the characteristic information corresponding to the related chemical components in the spectrum, which is often used for quantitative and qualitative analysis of the spectrum.It is widely used in quantitative analysis of matter and spectral classification and recognition.In this study, we collected the spectral data of normal articular cartilage samples from 8 weeks and 2 years of pathological changes, and used principal component analysis (PCA) algorithm and Fisher discriminant FDA-algorithm.Partial least square discriminant (PLS-DA) algorithm and support vector machine discriminant (SVM-DA) algorithm are used to construct discriminant models respectively to realize the classification and recognition of normal and pathological spectrum.The main content is: (1) based on the spectral pretreatment method, PLS-DA algorithm is used to classify and recognize the spectrum of normal and 2-year lesion groups.The prediction accuracy is 96.92 / 2) the unpretreated normal cartilage spectrum, the 8-week lesion spectrum and the 2-year lesion spectrum are classified and identified by PCA and FDA algorithm, respectively.Among them, the prediction accuracy of normal vs 8-week lesion group was 89.23 and that of normal vs 2-year lesion group was 92.31 and 92.31.The SVM-DA algorithm was used to distinguish the spectrum of normal 8-week lesion and 2-year lesion, and the overall prediction accuracy was 90.33%.When SVM-DA was used to distinguish the spectrum of normal vs 2 year lesion group, the prediction accuracy was 97. 7%.By comparing the classification results of the three models, it is found that the above three models can effectively realize the classification and recognition of normal and pathological spectrum.SVM-DA algorithm has the best classification effect and can effectively realize multi-class recognition of articular cartilage spectrum. It has the potential to develop into a new diagnosis method of early osteoarthritis and provide theoretical basis and data support for further research.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN219;R68

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本文編號(hào)：1762034