【摘要】：人體肺部氣道樹(shù)主要包括氣管、主支氣管及葉、段各級(jí)支氣管(約23級(jí)分支),是呼吸系統(tǒng)的重要組成部分,其結(jié)構(gòu)和功能的改變是呼吸系統(tǒng)疾病的重要原因和表現(xiàn)。MDCT(Multi-detector computed tomography,MDCT)是一種重要的、非創(chuàng)傷性氣道疾病評(píng)價(jià)手段。利用MDCT,一次屏氣即可獲得全肺連續(xù)和(或)重疊的、近似各向同性的高分辨率薄層(0.75-1.5 mm)結(jié)構(gòu)圖像。然而,MDCT獲取的海量數(shù)據(jù)對(duì)科學(xué)診斷和研究提出了巨大的挑戰(zhàn),需要利用先進(jìn)影像處理科學(xué)實(shí)現(xiàn)氣道自動(dòng)提取和結(jié)構(gòu)分析。本文的目的是提出一種適用于肺部氣道樹(shù)結(jié)構(gòu)分析的方法,實(shí)現(xiàn)肺部氣道樹(shù)骨架的自動(dòng)提取、標(biāo)記和定量化分析。本文使用自己建立的兩個(gè)模型和26組CT影像數(shù)據(jù)(均來(lái)自于中國(guó)醫(yī)科大學(xué)附屬盛京醫(yī)院)進(jìn)行算法驗(yàn)證。首先,利用建立的模型對(duì)拓?fù)浼?xì)化算法進(jìn)行了驗(yàn)證,并提出了分組連通性檢驗(yàn)的改進(jìn)方法。然后,將改進(jìn)后的細(xì)化算法應(yīng)用于從CT圖像中分割提出的氣道樹(shù)模型。具體地,在對(duì)圖像經(jīng)過(guò)形態(tài)學(xué)處理后,利用拓?fù)浼?xì)化方法提取肺部氣管骨架,對(duì)肺部氣管影像中的體素點(diǎn)進(jìn)行簡(jiǎn)單點(diǎn)判斷,利用歐拉示性數(shù)和連通性?xún)蓚�(gè)條件,將簡(jiǎn)單點(diǎn)刪除,得到肺部氣道樹(shù)的骨架。在肺部氣道樹(shù)骨架的基礎(chǔ)上,提取骨架的分叉點(diǎn)和葉子節(jié)點(diǎn),對(duì)不同級(jí)數(shù)的支氣管使用不同顏色進(jìn)行連線,得到肺部氣道樹(shù)的樹(shù)狀結(jié)構(gòu)。最后,在樹(shù)狀結(jié)構(gòu)的基礎(chǔ)上進(jìn)行支氣管長(zhǎng)度和分叉角度的測(cè)量。改進(jìn)后的拓?fù)浼?xì)化算法對(duì)建立的兩個(gè)模型可以得到完成正確的細(xì)化結(jié)果。另外,本文對(duì)26組數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)分析,成功提取所有數(shù)據(jù)的肺部氣道樹(shù)骨架,并且成功生成了 22組骨架的樹(shù)狀結(jié)構(gòu),其中樹(shù)狀結(jié)構(gòu)中沒(méi)有錯(cuò)誤分叉的骨架有14組。本文提取的肺部氣道樹(shù)樹(shù)狀結(jié)構(gòu)的級(jí)數(shù)最高可達(dá)到15級(jí),氣道樹(shù)的葉子節(jié)點(diǎn)最多有52個(gè)。最后,本文對(duì)所有數(shù)據(jù)中主支氣管進(jìn)行的量化分析發(fā)現(xiàn),左主支氣管的長(zhǎng)度平均值為76.43 mm,右主支氣管長(zhǎng)度平均值為37.06 mm,左右主支氣管之間夾角的平均值為109.76度。所得左右主氣管長(zhǎng)度和分叉角度與已有文獻(xiàn)報(bào)道一致。結(jié)果表明,本文提出的肺部氣道樹(shù)結(jié)構(gòu)分析的方法,對(duì)于部分肺部氣道樹(shù)可以實(shí)現(xiàn)骨架的自動(dòng)提取、標(biāo)記和定量化分析。肺部氣道樹(shù)的預(yù)處理程度對(duì)骨架提取至關(guān)重要,沒(méi)有空洞且表面光滑的肺部氣道樹(shù)是正確提取骨架的基礎(chǔ)。該方法對(duì)于氣道樹(shù)解剖結(jié)構(gòu)測(cè)量、氣道拓?fù)浣Y(jié)構(gòu)認(rèn)識(shí)和氣道疾病定量化診斷都具有一定的潛在價(jià)值。
[Abstract]:The human lung airway tree mainly consists of trachea, main bronchus, lobar and segmental bronchi (about 23 branches), which is an important part of the respiratory system. The structural and functional changes are important causes and manifestations of respiratory diseases. MDCT (Multi-detector computed tomography,MDCT) is an important and noninvasive assessment method for airway diseases. Continuous and (or) overlapping, approximately isotropic, high resolution thin slice (0.75 mm) structural images of the whole lung can be obtained by using MDCT, with one breath hold. However, the massive data obtained by MDCT poses a great challenge to scientific diagnosis and research. It is necessary to use advanced image processing science to realize automatic airway extraction and structural analysis. The purpose of this paper is to propose a method suitable for the structure analysis of pulmonary airway tree, and to realize the automatic extraction, marking and quantitative analysis of the pulmonary airway tree skeleton. In this paper, two models and 26 sets of CT image data (both from Shengjing Hospital, affiliated to China Medical University) are used to verify the algorithm. Firstly, the proposed model is used to verify the topology thinning algorithm, and an improved method of packet connectivity test is proposed. Then, the improved thinning algorithm is applied to the gas tree model of CT image segmentation. Specifically, after morphological processing of the image, the method of topological thinning is used to extract the trachea skeleton of the lung, and the voxel points in the image of the lung trachea are simply judged. The simple points are deleted by using two conditions: Euler number and connectivity. Get the skeleton of the lung airway tree. Based on the skeleton of the pulmonary airway tree, the bifurcation point and leaf node of the skeleton were extracted, and the tree-like structure of the pulmonary airway tree was obtained by connecting the bronchi of different order with different colors. Finally, the length and bifurcation angle of bronchus were measured on the basis of tree structure. The improved topology thinning algorithm can get the correct refinement results for the two models. In addition, 26 sets of data were analyzed experimentally, and the lung airway tree skeleton of all the data was successfully extracted, and 22 groups of tree structure were successfully generated. Among them, there were 14 groups of skeleton without error bifurcation in the tree structure. In this paper, the order of the tree-like structure of the lung airway tree is up to 15, and the number of leaf nodes of the tree is up to 52. Finally, a quantitative analysis of the main bronchus in all data shows that the average length of the left main bronchus is 76.43 mm, and the average length of the right main bronchus is 37.06 mm,. The average angle between the left and right main bronchi is 109.76 degrees. The length and bifurcation angle of the left and right main trachea are consistent with those reported in the literature. The results show that the method proposed in this paper can automatically extract, label and quantify the skeleton of some of the lung airway trees. The preprocessing degree of the lung airway tree is very important to the skeleton extraction. There is no cavity and smooth surface of the lung air channel tree is the basis for the correct extraction of the skeleton. This method is of potential value for the measurement of anatomical structure of airway tree, the recognition of airway topology and the quantitative diagnosis of airway diseases.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：R816.4;R56

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