本文關(guān)鍵詞： 肺結(jié)節(jié)分割 測地距離 光線投射 凸包操作 競爭擴散　出處：《東北大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來在世界范圍內(nèi)肺癌的發(fā)病率和死亡率持續(xù)攀升,發(fā)病率和死亡率已均居各類癌癥首位。針對肺癌還無法根治的現(xiàn)狀,降低其死亡率的主要措施是早發(fā)現(xiàn)、早診斷、早治療。肺癌在早期階段表現(xiàn)為肺結(jié)節(jié),肺結(jié)節(jié)通常比較小,很容易漏檢。CT成像技術(shù)是目前肺結(jié)節(jié)診斷和鑒別的首選方法,因其具有良好的密度分辨率、三維成像特征和很高的敏感度,可以很好顯示結(jié)節(jié)的位置、形態(tài)、大小、內(nèi)部結(jié)構(gòu)、密度、邊緣特征及其周圍的改變。目前,對肺結(jié)節(jié)良惡性的判定考慮的主要指標有體積、倍增率、形態(tài)等。肺結(jié)節(jié)的精確分割是進行結(jié)節(jié)定量分析和良惡性鑒定的前提和基礎(chǔ),因此,基于胸部CT圖像的結(jié)節(jié)分割已成為肺計算機輔助診斷技術(shù)中重要內(nèi)容之一。 由于結(jié)節(jié)自身的復(fù)雜性和CT成像技術(shù)的特點,不同的結(jié)節(jié)在灰度值、形態(tài)、與周圍組織粘連程度等方面表現(xiàn)出很大差異,使得結(jié)節(jié)的分割成為一項極具挑戰(zhàn)性的工作。針對結(jié)節(jié)的分割,本文提出了三種不同的分割方法,前兩種能夠?qū)μ囟ǚN類的結(jié)節(jié)進行分割,第三種能夠?qū)Σ煌N類的結(jié)節(jié)進行分割,包括：(1)基于局部形狀分析的血管粘連型結(jié)節(jié)分割方法,首先利用距離變換找到結(jié)節(jié)的內(nèi)核,然后利用測地距離作為步長進行區(qū)域生長,探測血管和結(jié)節(jié)發(fā)生粘連的位置,剔除血管；(2)基于三維光線投射的肺壁粘連型結(jié)節(jié)分割方法,首先利用光線投射探測靠近肺實質(zhì)一側(cè)結(jié)節(jié)的輪廓,然后利用凸包操作對探測到的輪廓進行重建；(3)基于最速下降思想的肺結(jié)節(jié)分割方法,首先利用競爭擴散系統(tǒng)對圖像進行二值化,距離變換找到結(jié)節(jié)內(nèi)核,然后從結(jié)節(jié)的內(nèi)核開始進行最速下降,找到結(jié)節(jié)的邊界,實現(xiàn)不同種類結(jié)節(jié)的分割。通過實驗驗證,這三種算法對結(jié)節(jié)的分割均取得較高的準確率和效率,和金標準的平均重疊率依次達到85.16%、93.03%和85.35%,平均分割一個結(jié)節(jié)的時間為2s、12.7s和1.5s。表明了這些算法的分割結(jié)果可以用于結(jié)節(jié)的定量分析,并為結(jié)節(jié)的診斷和治療提供輔助信息。
[Abstract]:In recent years, the incidence and death rate of lung cancer has been rising continuously in the world, and the morbidity and mortality have been ranked first in all kinds of cancers. In view of the present situation that lung cancer cannot be cured, the main measures to reduce the mortality rate are early detection and early diagnosis. Early treatment. Lung cancer in the early stage of pulmonary nodules, pulmonary nodules are usually relatively small, it is easy to miss the CT imaging technology is the first choice for the diagnosis and differentiation of pulmonary nodules, because of its good density resolution, Three-dimensional imaging features and high sensitivity can well display the location, shape, size, internal structure, density, marginal features and the changes around the nodules. At present, the main criteria for judging benign and malignant pulmonary nodules are volume. The accurate segmentation of pulmonary nodules is the premise and foundation of quantitative analysis and identification of benign and malignant nodules. Therefore, the segmentation of nodules based on chest CT images has become one of the important contents of computer-aided diagnosis of lung. Due to the complexity of the nodules themselves and the characteristics of CT imaging techniques, different nodules show great differences in terms of gray value, morphology, adhesion to the surrounding tissues, and so on. In this paper, we propose three different segmentation methods, the first two can segment a specific type of nodules. The third one is able to segment different types of nodules, including: 1) a method for segmenting vascular adherent nodules based on local shape analysis. Firstly, distance transformation is used to find the kernel of the nodules, and then geodesic distance is used as the step size for regional growth. To detect the location of adhesion between blood vessels and nodules, and to remove the blood vessels. (2) the segmentation method of pulmonary wall adhesion nodules based on 3D ray casting. First, the contours of nodules near one side of the lung parenchyma were detected by ray casting. Then the detected contour is reconstructed by convex hull operation. Based on the idea of steepest descent, the lung nodule is segmented. Firstly, the image is binarized by competitive diffusion system, and the kernel of the nodule is found by distance transformation. Then starting from the kernel of the nodule, the fastest descent is carried out, the boundary of the nodule is found, and the segmentation of different kinds of nodule is realized. The experimental results show that these three algorithms achieve high accuracy and efficiency in the segmentation of the nodule. The average overlap rate of the standard and the gold standard was 85.16% and 85.35% respectively, and the average time of dividing a single node was 2 s-1. 7 s and 1. 5 s. The results showed that the segmentation results of these algorithms could be used for quantitative analysis of nodules and provide auxiliary information for the diagnosis and treatment of nodules.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R734.2;R730.44

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相關(guān)期刊論文 前1條

1 孫申申;李宏;侯欣然;康雁;趙宏;;基于EM和Mean-shift的肺結(jié)節(jié)分割[J];中國圖象圖形學(xué)報;2009年10期

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