本文選題：肺氣管道 + 細化��； 參考：《東北大學》2012年碩士論文

【摘要】：在全球范圍內,呼吸系統(tǒng)疾病是一類重要的非傳染性流行疾病。多探測器計算機斷層成像(MDCT)是一種有效的、非創(chuàng)傷性氣道疾病研究和評價手段。但由于缺乏氣管結構分析方法研究,在揭示疾病的本質、特征和發(fā)生、發(fā)展規(guī)律時仍面臨挑戰(zhàn)。在此背景下,計算機輔助診斷(computer aided diagnosis, CAD)的研究蓬勃發(fā)展起來。肺氣管道的自動分割與骨架化以及對肺氣管相關疾病的自動診斷系統(tǒng)是當前CAD系統(tǒng)的一個重要分支。 本課題的目的是提出并實現(xiàn)一種自動的氣管骨架化算法,從而提取氣道中心線,生成氣管樹狀結構圖,獲取氣管2D橫截面,為氣管結構的定量化分析提供方法。 研究中的CT影像數(shù)據(jù)均來自于中國醫(yī)科大學附屬盛京醫(yī)院(中國,沈陽),飛利浦Brillance64CT系統(tǒng),重建矩陣512*512�；谟蒀T影像數(shù)據(jù)中提取的二值化氣道樹,提出拓撲細化算法,通過簡單點判斷不斷從邊界上刪除多余體素,直至剩下單體素寬的骨架。再設定合適閾值,將骨架線上的細化“毛刺”剪除。從最終骨架中識別氣道根點、分叉點、線端點和線上點,利用二叉樹拓撲結構建立氣管樹狀結構圖,實現(xiàn)氣道級數(shù)標記。同時利用提取的氣道根點、分叉點和線端點建立簡化的樹狀模型。最后,利用三次B樣條曲線對氣道中心線平滑后,計算中心線切向量(即氣道橫截面法向量),從而通過坐標變換獲取2D橫斷面圖像數(shù)據(jù),為將來氣管直徑測量和氣管壁厚度分析打下基礎。 本課題提出的算法能成功提取10組數(shù)據(jù)中的氣道骨架,剪枝閾值設置為15較為合適。獲得的支氣管樹結構圖和簡化樹狀模型可實現(xiàn)自動命名,最高可標記至12級支氣管,總支氣管分支數(shù)量最多可達174個。最后,快速準確提取各級支氣管的2D橫斷面圖像數(shù)據(jù)。 綜上所述,本課題提出的自動氣管骨架化算法可以用于氣管的定量化結構分析,為揭示氣道疾病的本質、特征和發(fā)生、發(fā)展規(guī)律提供幫助。
[Abstract]:Globally, respiratory diseases are an important class of non-communicable endemic diseases. Multi-detector computed tomography (MDCT) is an effective and non-traumatic method for the study and evaluation of airway diseases. However, due to the lack of tracheal structure analysis method, it is still facing challenges in revealing the nature, characteristics, occurrence and development of the disease. Under this background, the research of computer aided diagnosis (aided diagnosis, CAD) is booming. The automatic segmentation and skeleton of the pulmonary duct and the automatic diagnosis system for the diseases related to the pulmonary duct are an important branch of the current CAD system. The purpose of this paper is to propose and implement an automatic trachea skeleton algorithm, so as to extract the central line of the airway, generate the trachea tree structure diagram, obtain the trachea 2D cross section, and provide a method for the quantitative analysis of the trachea structure. The CT image data in the study were obtained from Shengjing Hospital affiliated to China Medical University (Shenyang, China, Philips Brillance64CT system, reconstruction matrix 512 / 512). Based on the binary airway tree extracted from CT image data, a topological thinning algorithm is proposed. The redundant voxels are continuously deleted from the boundary by simple point judgment until the skeleton with a single element width is left. Then set the appropriate threshold, the skeleton line on the fine "burr" cut off. The airway root points, bifurcation points, line endpoints and line points are identified from the final skeleton, and the trachea tree structure diagram is established by using the binary tree topology to mark the airway series. At the same time, a simplified tree model is established using the extracted airway root points, bifurcation points and line endpoints. Finally, after the cubic B-spline curve is used to smooth the central line of the airway, the tangent vector of the center line (i.e. the normal vector of the airway cross-section) is calculated, and the 2D cross-sectional image data are obtained by coordinate transformation. It will lay a foundation for the measurement of trachea diameter and the analysis of the wall thickness of trachea in the future. The proposed algorithm can extract the airway skeleton from 10 groups of data successfully and set the pruning threshold to 15. The obtained bronchotree structure diagram and simplified tree model can be automatically named. The highest number of bronchi can be labeled to grade 12, and the number of branches of the total bronchus can be up to 174. Finally, 2D cross-sectional image data of all levels of bronchus were extracted quickly and accurately. To sum up, the automatic trachea skeleton algorithm proposed in this paper can be used to analyze the quantitative structure of trachea, which can help to reveal the nature, characteristics, occurrence and development of airway diseases.
【學位授予單位】：東北大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R816.41;TP391.41

【參考文獻】

相關期刊論文 前1條

1 竺海;姬紅兵;高新波;;基于邊界距離場的管腔中心路徑自動提取算法[J];計算機輔助設計與圖形學學報;2006年06期

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本文編號：1988497