發(fā)布時間：2018-08-23 08:42

【摘要】：隨著飲食環(huán)境問題的不斷加重，近年來，肺部疾病的發(fā)病率也呈不斷上升的事態(tài)，已然成為了當(dāng)前影響人類生活質(zhì)量甚至生命的大敵因此，如何能夠在病變早期就能準(zhǔn)確的診斷出病變良惡性質(zhì)，成為我們能夠大大減低肺癌發(fā)病率的最有效的手段，同時也成了當(dāng)前的研究熱點之一在病變的最早期，肺部疾病通常在影像學(xué)上表現(xiàn)為肺結(jié)節(jié)，同時，良惡性結(jié)節(jié)在影像學(xué)的征象上也有極大的差異性本文中主要是基于孤立性肺結(jié)節(jié)良惡性不同的影像學(xué)征象進(jìn)而實現(xiàn)早期良惡性鑒別 基于PET/CT的混合成像技術(shù)對肺癌進(jìn)行醫(yī)學(xué)診斷，在考慮肺結(jié)節(jié)的臨床征象的基礎(chǔ)上，充分結(jié)合了肺結(jié)節(jié)的PET征象和CT征象，較好的克服了單一圖像對結(jié)節(jié)診斷信息不足的缺點目前，對早期肺結(jié)節(jié)良惡性的判別仍是依賴醫(yī)師的閱片經(jīng)驗，而且所依據(jù)的判別特征不能量化，難免會出現(xiàn)漏診誤診的情況為了能夠盡可能的在前期減少由于主觀因素而造成的漏診誤診的現(xiàn)象，需要對結(jié)節(jié)各個征象進(jìn)行量化，依據(jù)肺結(jié)節(jié)各個影像學(xué)征象之間的相關(guān)性建立預(yù)測肺結(jié)節(jié)良惡性的數(shù)學(xué)模型分析各個征象之間的相關(guān)度以及與肺結(jié)節(jié)良惡性的關(guān)系，從而構(gòu)建一個能夠預(yù)測結(jié)節(jié)良惡性的模型 本文立足于對孤立性肺結(jié)節(jié)進(jìn)行良惡性預(yù)測的這一課題，主要的研究工作從以下幾個方面展開： 1．利用一種改進(jìn)的支持向量機(jī)——雙向隸屬度的模糊支持向量機(jī)的方法對孤立性肺結(jié)節(jié)良惡性進(jìn)行分類本文的終極目標(biāo)是實現(xiàn)對孤立性肺結(jié)節(jié)進(jìn)行良惡性分類，利用傳統(tǒng)的支持向量機(jī)對肺結(jié)節(jié)進(jìn)行良惡性分類時，認(rèn)為所有的樣本對獲得最優(yōu)超分類面的貢獻(xiàn)是相同的，沒有考慮到樣本之間的相關(guān)性對分類面的影響本文基于雙向隸屬度的模糊支持向量機(jī)的分類方法在對肺結(jié)節(jié)進(jìn)行良惡性分類過程中充分考慮了不同樣本點對分類結(jié)果的貢獻(xiàn)，基于當(dāng)前影像學(xué)中對肺結(jié)節(jié)良惡性進(jìn)行診斷的比較成熟的規(guī)則，并充分考慮結(jié)節(jié)的CT PET圖像征象及病變的臨床征象，實現(xiàn)對孤立性肺結(jié)節(jié)良惡性的準(zhǔn)確分類； 2．構(gòu)建一個能充分考慮肺結(jié)節(jié)的PET和CT征象的良惡性預(yù)測的模型對所提取的結(jié)節(jié)的CT和PET征象進(jìn)行量化，通過單因素分析法分析每個結(jié)節(jié)的征象與良惡性之間的關(guān)系，篩選出具有顯著相關(guān)性的征象，，然后再基于所篩選出的各個因素構(gòu)建能夠預(yù)測結(jié)節(jié)良惡性的回歸方程 最后，本論文還對所涉及的方法進(jìn)行了實驗，并驗證各個方法的有效性，實驗結(jié)果參數(shù)證明，本文的方法在鑒別肺結(jié)節(jié)良惡性方面具有較好的性能，在保證準(zhǔn)確率的同時降低了檢測的漏診率，也在一定程度上體現(xiàn)了方法的泛化性
[Abstract]:With the increasing problem of diet and environment, in recent years, the incidence of lung diseases has been on the rise, which has become a major enemy affecting the quality of human life and even life. How to accurately diagnose the benign and malignant nature of the disease at the early stage of the disease has become the most effective means for us to reduce the incidence of lung cancer greatly. At the same time, it has also become one of the current research hotspots in the early stage of the disease. Lung disease is usually shown as a pulmonary nodule on imaging, and at the same time, There is also a great difference in imaging signs between benign and malignant nodules. In this paper, the imaging features of benign and malignant solitary pulmonary nodules are mainly based on the different imaging signs, so as to realize the early differentiation of benign and malignant nodules based on mixed imaging based on PET/CT. Technology for medical diagnosis of lung cancer, On the basis of considering the clinical features of pulmonary nodules, the PET and CT signs of pulmonary nodules are fully combined to overcome the shortcomings of single image in diagnosis of nodules. The diagnosis of benign and malignant pulmonary nodules is still dependent on the physician's experience in film reading, and the discriminant characteristics on which they are based cannot be quantified. In order to reduce the misdiagnosis caused by subjective factors in the early stage, it is necessary to quantify the various signs of the nodules. According to the correlation between various imaging signs of pulmonary nodules, a mathematical model was established to predict the benign and malignant pulmonary nodules. So as to build a model that can predict benign and malignant nodules, this paper is based on the subject of predicting benign and malignant solitary pulmonary nodules. The main research works are as follows: 1. Using an improved support vector machine-fuzzy support vector machine with bidirectional membership to classify the benign and malignant solitary pulmonary nodules. The ultimate goal is to achieve benign and malignant classification of solitary pulmonary nodules When using traditional support vector machine to classify lung nodules from benign and malignant, it is considered that all samples have the same contribution to obtaining the optimal super-classification surface. In this paper, fuzzy support vector machine based on bidirectional membership degree is used to classify lung nodules. In the process of classification of benign and malignant nodules, the contribution of different sample points to classification results is fully considered. Based on the mature rules for the diagnosis of benign and malignant pulmonary nodules in current imaging, and taking into account the CT PET imaging signs and the clinical signs of lesions, the accurate classification of benign and malignant solitary pulmonary nodules can be realized. 2.Construct a model that can fully consider the PET and CT signs of pulmonary nodules, and quantify the CT and PET signs of the extracted nodules. Single factor analysis was used to analyze the relationship between the signs of each nodule and the benign and malignant ones, and the signs with significant correlation were screened out. Then, based on the selected factors, the regression equation is constructed to predict the benign and malignant nodules. Finally, the methods involved are tested, and the validity of the methods is verified. The method in this paper has good performance in differentiating benign and malignant pulmonary nodules. It not only ensures the accuracy but also reduces the missed diagnosis rate of detection. It also reflects the generalization of the method to a certain extent.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP18;O212.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前4條

1 錢可寶;張勇;黃云超;;孤立性肺結(jié)節(jié)的外科治療分析[J];中國肺癌雜志;2007年04期

2 董莘;;孤立性肺結(jié)節(jié)影像學(xué)診斷和處理[J];沈陽部隊醫(yī)藥;2006年06期

3 王利偉;王自正;顧建平;王麗萍;馮敏;王紹娟;;孤立性肺結(jié)節(jié)的CT診斷[J];醫(yī)學(xué)影像學(xué)雜志;2006年08期

4 王鋒;王藏海;朱斌;;孤立性肺結(jié)節(jié)的綜合CT診斷[J];河北北方學(xué)院學(xué)報;2006年06期

本文編號：2198518