本文選題：肺氣道　切入點(diǎn)：計(jì)算機(jī)輔助診斷　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2010年博士論文　論文類型：學(xué)位論文

【摘要】： 肺部疾病是人類健康的重要威脅,建立肺部疾病計(jì)算機(jī)輔助診斷(CAD)系統(tǒng)已成為當(dāng)前的研究熱點(diǎn)。目前,有兩種不同的實(shí)現(xiàn)肺部疾病CAD系統(tǒng)的方案：傳統(tǒng)的異常依賴方案(ADA)和新興的正常依賴方案(NDA)。異常依賴方案關(guān)注于圖像目標(biāo)中符合某種特定病變特征的區(qū)域,因此不能很好地應(yīng)對(duì)臨床診斷中多種病變同時(shí)存在的情況。正常依賴方案則更加符合專業(yè)醫(yī)師的讀片方法和診斷思路,那就是先識(shí)別并排除圖像中的正常區(qū)域,然后對(duì)剩下的可能異常的區(qū)域進(jìn)行精細(xì)分析。這樣,多種病變的信息可以同時(shí)被保留下來(lái),而不僅僅是某一種特定病變。鑒于這個(gè)原因,正常依賴方案已經(jīng)成為未來(lái)肺部疾病CAD系統(tǒng)研究的發(fā)展趨勢(shì)。 本文遵循正常依賴方案的基本思想和原則,提出了一條新穎的構(gòu)建人體肺部疾病CAD系統(tǒng)的技術(shù)路線,其基礎(chǔ)性工作是在計(jì)算機(jī)里建立一個(gè)反映正常人群影像學(xué)特征的肺部數(shù)字化參數(shù)集合。我們將注意力主要放在肺氣道參數(shù)上,因?yàn)榉螝獾兰膊∈亲顕?yán)重的肺部疾病之一,而且我們的工作將顯示出肺氣道參數(shù)能夠從一定程度上反映整個(gè)肺的情況。本文最終建立了一個(gè)包含豐富肺氣道參數(shù)的數(shù)學(xué)模型,并通過(guò)五個(gè)步驟來(lái)達(dá)成這一目標(biāo)。在每個(gè)步驟中,都進(jìn)行了深入的研究工作,取得了一定的成果。 第1步：肺分割。肺分割是后續(xù)肺氣道分割的基礎(chǔ)。針對(duì)醫(yī)學(xué)圖像背景復(fù)雜、邊界模糊、局部不均勻等特點(diǎn),提出使用相對(duì)模糊連接度作為幾何主動(dòng)輪廓模型中曲線演化的驅(qū)動(dòng)力,并從理論分析和實(shí)驗(yàn)驗(yàn)證兩個(gè)角度證明了其對(duì)于肺部圖像的適用性。本文方法在多目標(biāo)圖像和復(fù)雜圖像的分割實(shí)驗(yàn)中取得了良好的效果,最終分割出了完整、正確的肺組織。 第2步：氣道分割。氣道分割是整個(gè)模型建立的基礎(chǔ),并直接決定著模型的性能。以普通分割方法的結(jié)果作為本文分割的基礎(chǔ),采用氣道分割的一般性框架,對(duì)其中的分割、評(píng)價(jià)等模塊進(jìn)行了改進(jìn)。對(duì)于泄漏和細(xì)小氣道提取這兩大難題,提出了有針對(duì)性的策略。最終提取了10個(gè)層級(jí)中的104個(gè)氣道段(相當(dāng)于手動(dòng)分割數(shù)量的45%),并完整保留了段支氣道以上的全部氣道信息。 第3步：骨架提取。提取肺氣道樹(shù)的單像素寬中心骨架是進(jìn)行參數(shù)測(cè)算的必須環(huán)節(jié),幾乎所有結(jié)構(gòu)性參數(shù)的定義和測(cè)算方法都是基于氣道骨架而提出的。針對(duì)肺氣道樹(shù)骨架化的特定需求,綜合分析了常用的四類骨架提取算法,并最終選擇了層次性一般勢(shì)能場(chǎng)方法。整個(gè)骨架化過(guò)程被分為提取核心骨架(第一級(jí))、加入細(xì)小分支(第二級(jí))、連接末梢端點(diǎn)(第三級(jí))這三個(gè)完整性逐漸增加的層級(jí),其中第三級(jí)的結(jié)果即是最終結(jié)果。這種方法的骨架化效果在完整性上要優(yōu)于其他方法。 第4步：參數(shù)測(cè)算。這是與建立數(shù)學(xué)模型直接相關(guān)的步驟,所測(cè)算出的參數(shù)就是數(shù)學(xué)模型中的數(shù)據(jù)部分。整個(gè)肺氣道樹(shù)被解剖為整體、層級(jí)、段和層片四種結(jié)構(gòu),針對(duì)每種結(jié)構(gòu)提取了一類參數(shù)。整體類參數(shù)有4種,層級(jí)類參數(shù)有4種(10層級(jí)),段類參數(shù)有5種(104段),層片類參數(shù)有5種(1916層片),因此總共提取了10144個(gè)肺氣道參數(shù)。部分參數(shù)與影像學(xué)事實(shí)和真實(shí)解剖值進(jìn)行了對(duì)比,結(jié)果顯示這些測(cè)算結(jié)果是正確、可信的。 第5步：模型建立。這是本文的最終目標(biāo)。建立出的數(shù)學(xué)模型由兩個(gè)部分組成：數(shù)據(jù)和操作。所有肺氣道參數(shù)數(shù)據(jù)存儲(chǔ)在具有五級(jí)結(jié)構(gòu)的樹(shù)中。樹(shù)的每一級(jí)由一種類型的結(jié)構(gòu)體組成,每種結(jié)構(gòu)體分別存儲(chǔ)整體參數(shù)、層級(jí)參數(shù)、段參數(shù)、層片參數(shù)和體素參數(shù)。對(duì)數(shù)據(jù)的操作是模型的第二部分,是對(duì)參數(shù)進(jìn)行計(jì)算、存儲(chǔ)、裝載、查詢、調(diào)用等的計(jì)算機(jī)算法。結(jié)構(gòu)體中存有編碼和指針,用于參數(shù)的查詢和調(diào)用。在模型數(shù)據(jù)和模型操作的基礎(chǔ)上,給出了三個(gè)實(shí)際應(yīng)用的解決方案,作為后續(xù)建立肺部疾病CAD系統(tǒng)的參考。 總之,本文所構(gòu)建出的人體肺氣道樹(shù)數(shù)學(xué)模型中含有豐富而正確的參數(shù),可以用于下一步建立肺部疾病CAD系統(tǒng)。 本文作者特別感謝國(guó)家自然科學(xué)基金(60771007)和中科院研究生科技創(chuàng)新基金(2008年度)對(duì)本文的資助。
[Abstract]:Lung disease is an important threat to human health, the establishment of lung disease computer aided diagnosis (CAD) system has become the focus of current research. At present, a realization of lung disease CAD system for two kinds of abnormal dependence of traditional scheme (ADA) and the new normal dependent scheme (NDA) ADA focused. On the image with a specific lesion area, so it can not deal with at the same time, the presence of multiple lesions in clinical diagnosis. The NDA is more in line with the medical professional film reading method and diagnostic thinking, that is to identify and exclude the normal image area, then the remaining possible fine analysis the abnormal area. In this way, a variety of disease information can also be retained, rather than a particular disease. For this reason, the normal scheme has become dependent on the future The development trend of CAD system in lung disease.
This paper follows the basic ideas and principles of NDA, proposed a novel construction of human lung disease CAD system technical route, the basic work is to establish a set of lungs digital parameters reflect the normal people in the computer image features. We will focus mainly on parameters of lung airway, because of pulmonary airway the disease is the most serious lung diseases, and our work will show that airway parameters can reflect the entire lung from a certain extent. This paper established a mathematical model with rich airway parameters, and through the five steps to achieve this goal. In each step, are carried out deep research work, and achieved certain results.
The first step: lung segmentation. Lung segmentation is the basis for subsequent airway segmentation. For medical image with complex background, fuzzy boundaries, characteristics of local inhomogeneity, put forward relative fuzzy connectedness as a geometric active contour model driven curve evolution, and from the theoretical analysis and experimental verification of two proved that the the applicability of the lung images. This method has a good effect in multi object image segmentation experiments and complex image segmentation, finally the integrity, the right lung tissue.
The second step: airway segmentation. Airway segmentation is the basis of the model establishment, and directly determines the performance of the model. The common segmentation method results as the basis for this division, the general framework of the airway segmentation, segmentation, evaluation module is improved. For small airway leakage and extraction of the two the problem, proposed a targeted strategy. Finally from the 104 section of the 10 levels in the airway (equivalent to 45% of the number of manual segmentation), and retain the integrity of the airway section supporting the whole airway information above.
The third step: skeleton extraction. Single pixel wide skeleton extraction center airway tree is the essential part for parameters measurement, definition and calculation method of almost all structural parameters are proposed. Based on the skeleton of airway for the needs of specific airway tree skeleton, a comprehensive analysis of the four kinds of commonly used skeleton extraction algorithm, and chose the level of general potential field method. The whole skeleton extraction process is divided into core skeleton (first level), with small branches (second), connecting peripheral points (third) of the three integrity level gradually increased, the third level is the result of the final result. The effect of this skeleton the method is better than other methods in integrity.
The fourth step: parameter estimates. This is directly related to the establishment of mathematical model of the steps, the data portion of the measured parameters is the mathematical model. The airway tree was dissected as a whole, level, structure of four kinds of segments and layers for each extraction structure for a class of parameters. The whole class has 4 parameters a class hierarchy, there are 4 parameters (10 levels), 5 kinds of segment parameters (104), 5 kinds of slice parameters (1916 slices), so the total extraction of 10144 pulmonary airway parameters. Some parameters and imaging anatomic facts and real values were compared, the results showed these calculation results are correct and reliable.
The fifth step: to establish the model. This is the ultimate goal of this paper. The established model consists of two parts: data and operation. All the parameters of lung airway data stored in a structure with five levels in the tree. The tree of each level by a type of structure body, each storage structure respectively. The overall level of parameters, parameters, parameters, slice parameters and voxel parameters. The operation of the data is the second part of the model, the calculation of parameters, loading, storage, query, computer algorithm calls etc. the structure being encoding and pointers, query and call for parameters based on the model. The data and operation of the model, gives three solutions for practical applications, as the subsequent establishment of lung disease CAD system.
In conclusion, the mathematical model of the human lung airway tree constructed in this paper contains abundant and correct parameters, which can be used to establish the CAD system for the next step of the lung disease.
The author of this article is particularly grateful to the National Natural Science Foundation (60771007) and the graduate science and Technology Innovation Fund (2008) of the Chinese Academy of Sciences for the funding of this article.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2010
【分類號(hào)】：R311;TP391.41

【引證文獻(xiàn)】

相關(guān)博士學(xué)位論文 前1條

1 王興家;基于4D-CT數(shù)據(jù)的心臟重構(gòu)方法研究[D];中國(guó)科學(xué)技術(shù)大學(xué);2011年

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本文編號(hào)：1624546