本文選題：磨玻璃密度影　切入點(diǎn)：肺實(shí)質(zhì)分割　出處：《哈爾濱工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：肺癌是嚴(yán)重威脅廣大人民群眾的重大疾病。僅2012年,肺癌就奪走了109.8萬(wàn)男性,49萬(wàn)女性的生命。肺癌的早期確診是延長(zhǎng)肺癌患者五年生存率的有效途徑,同時(shí)也是減輕患者家庭負(fù)擔(dān)的最有效辦法。磨玻璃密度影(Ground Glass Opacity,GGO)是肺癌在CT影像中的重要早期表現(xiàn),但同時(shí)也是一種非特異性表現(xiàn)。目前,GGO的良惡性判斷主要是通過檢測(cè)和分析GGO的形態(tài)學(xué)特征(如大小、形狀、邊緣、粘連結(jié)構(gòu)、是否內(nèi)含實(shí)質(zhì)性成分等)及特征的生長(zhǎng)變化來進(jìn)行,GGO的準(zhǔn)確分割是以上工作的重要前提。當(dāng)前,已經(jīng)有部分GGO分割算法被提出,但是其分割效果均不太理想。尤其是當(dāng)前的分割算法主要以分割出GGO的主體部分為目標(biāo),忽略了GGO的邊緣結(jié)構(gòu)(如毛刺、條索等),不僅造成了目標(biāo)對(duì)象的過分割,影響了GGO面積的測(cè)量,而且會(huì)造成后期GGO特征分析的偏差、GGO良惡性判斷的錯(cuò)誤。本文提出了基于區(qū)域自適應(yīng)權(quán)重的馬爾科夫隨機(jī)場(chǎng)模型的GGO分割算法,實(shí)現(xiàn)了GGO較為準(zhǔn)確的分割。但該模型中的正常肺實(shí)質(zhì)灰度分布模型參數(shù)嚴(yán)重依賴于訓(xùn)練樣本,GGO的灰度分布模型參數(shù)需要依據(jù)GGO的初分割結(jié)果進(jìn)行更新,兩者都會(huì)影響到實(shí)驗(yàn)的運(yùn)算速度和分割結(jié)果準(zhǔn)確性。為此,本文進(jìn)一步提出了基于馬爾科夫隨機(jī)場(chǎng)的二維大津法模型和混合類泊松模型來實(shí)現(xiàn)GGO的分割。本文內(nèi)容分為以下四個(gè)部分:1、肺實(shí)質(zhì)的自動(dòng)分割;2、基于區(qū)域自適應(yīng)權(quán)重的馬爾科夫隨機(jī)場(chǎng)(Markov Random Field,MRF)模型;3、基于MRF的二維大津法模型;4、基于MRF的有限混合類泊松模型。肺實(shí)質(zhì)的準(zhǔn)確分割能夠有效的排除胸部CT圖像內(nèi)與GGO分割、特征分析無(wú)關(guān)因素(如CT檢查床、衣物、胸壁、縱膈等)的影響,減少計(jì)算量和計(jì)算誤差,提高GGO的分割準(zhǔn)確性。本文在肺實(shí)質(zhì)的分割過程中首先使用基于OSTU(大津法)的閾值法確定分割閾值、利用連通域標(biāo)記法填充肺實(shí)質(zhì)區(qū)域內(nèi)空洞、借助投影法確定左右肺實(shí)質(zhì)是否未分離及ROI區(qū)域;然后基于滾球法和凸包法來實(shí)現(xiàn)肺實(shí)質(zhì)的邊緣平滑修補(bǔ)。從而得到較為完整的肺實(shí)質(zhì)分割結(jié)果。基于區(qū)域自適應(yīng)權(quán)重的MRF模型是指該馬爾科夫隨機(jī)場(chǎng)中的轉(zhuǎn)移概率是一個(gè)基于局部隸屬度的自適應(yīng)參數(shù)。該參數(shù)在隸屬于GGO的隸屬度與隸屬于正常肺實(shí)質(zhì)區(qū)域的隸屬度有較大差別的局部區(qū)域內(nèi)取得較大的值,在隸屬于GGO的隸屬度與隸屬于正常肺實(shí)質(zhì)的隸屬度較為接近的區(qū)域內(nèi)取較小的值。基于該模型,GGO得到了較為準(zhǔn)確的分割�；贛RF的二維大津法是在二維大津法的類間方差計(jì)算公式中引入了基于MRF的平滑能量。該能量能夠有效的調(diào)節(jié)使用最優(yōu)閾值得到的GGO潛在區(qū)域的個(gè)數(shù)及潛在區(qū)域的大小,有效的抑制過小肺紋理的影響、減少后期GGO識(shí)別過程的工作量。基于MRF的有限混合類泊松模型是在考慮像素點(diǎn)的空間關(guān)系的前提下,使用有限個(gè)類泊松模型對(duì)肺實(shí)質(zhì)的灰度分布進(jìn)行擬合的一個(gè)算法模型。它充分利用了正常肺實(shí)質(zhì)、GGO、高密度肺紋理等區(qū)域的灰度分布類似于泊松分布(或偏正態(tài)分布)的性質(zhì),能夠更好的對(duì)像素點(diǎn)進(jìn)行歸類。本文通過專家打分法來評(píng)價(jià)基于該三種算法的GGO分割結(jié)果的優(yōu)劣,通過計(jì)算分割結(jié)果的誤差率、面積和形狀的測(cè)量精度來分別對(duì)比評(píng)價(jià)該算法與同類算法的優(yōu)劣。實(shí)驗(yàn)表明基于以上三種方法,都取得了較同類算法更好的GGO分割結(jié)果。
[Abstract]:Lung cancer is a major disease that threatens the people. Only in 2012, lung cancer took 1 million 98 thousand men and 490 thousand women's life. The early diagnosis of lung cancer is a effective way to prolong the five year survival rate of patients with lung cancer, the most effective way is to reduce the family burden of patients. Ground glass opacity (Ground Glass, Opacity, GGO) is an important early manifestation of lung cancer in the CT image, but also a nonspecific performance. At present, the judgment of malignant and benign GGO is mainly through the morphological feature detection and analysis of GGO (such as size, shape, edge, adhesion structure, whether contains substantive components) growth characters and changes to. Accurate segmentation of GGO is an important prerequisite for the above work. At present, there have been some GGO segmentation algorithms have been proposed, but the segmentation results are not ideal. Especially the current segmentation algorithms to segment the main GGO The body part is the goal, ignore the edge structure of GGO (such as burr, cable, etc.) not only caused the over segmentation of the target object, affect the measurement of GGO area, but also cause the deviation analysis of late GGO features, GGO diagnosis error. This paper proposes MRF model GGO segmentation algorithm based on Markov region adaptive weights, to achieve the GGO accurate segmentation. But the model of normal lung parenchyma gray distribution of model parameters depends heavily on training samples, parameters of gray distribution model according to the GGO GGO at the beginning of the segmentation results in the update, operation speed and accuracy of the segmentation results will affect the experiment. Therefore in this paper, further put forward the Markov random field model and two-dimensional Otsu method mixed Poisson model to achieve segmentation based on GGO. This paper is divided into the following four parts: 1, automatic lung parenchyma 2, regional segmentation; Markov adaptive weighted random field (Markov Random Field, 3, MRF) model; two-dimensional Otsu method model based on MRF; 4, MRF finite mixture Poisson model based on the accurate segmentation of lung parenchyma segmentation and GGO can remove the chest CT images effectively, analysis of the characteristics of independent factors (check the bed clothes, such as CT, chest wall, mediastinum etc.) effect, reduce the computation error, improve the segmentation accuracy of GGO. Based on the segmentation of lung parenchyma in the first use of OSTU based (Otsu) to determine the threshold of the threshold method, the connected domain labeling filling lung parenchyma area empty, according to the projection method to determine the left and right lung parenchyma is not isolated and ROI region; then based on the rolling ball method and convex hull method to achieve smooth edge repair of lung parenchyma. In order to get a more complete lung segmentation results. Based on the regional adaptive weight MRF model refers to the transition probability of Markov field is a local adaptive parameter based on degree of membership. To achieve greater value of local area of the GGO in which the parameters belong to membership membership and belongs to the normal lung parenchyma area has a larger difference in the smaller regional membership degree belongs to GGO the degree of belonging to normal lung parenchyma is close to the internal value. Based on this model, GGO obtained a more accurate segmentation. The two-dimensional Otsu method based on MRF is the variance in two-dimensional Otsu method between class calculation formula was introduced in MRF. Based on the number of smooth energy and potential energy of the area effectively the regulation of GGO using the optimal threshold potential area to the size of the effective inhibition effect of small pulmonary veins, reduce the late GGO recognition process work. MRF finite mixture model based on Poisson is considered as In the premise of spatial relations, an algorithm of gray model of lung parenchyma distribution was fitted using finite Poisson model. It makes full use of the normal lung parenchyma, GGO, high density lung texture regions gray distribution similar to the Poisson distribution (or partial normal distribution) properties can be better the classification of pixels. In this paper, by using expert scoring method to evaluate the segmentation results of three algorithms based on the merits of GGO, through the calculation results of segmentation error rate, accuracy of size and shape were compared to evaluate the algorithm with the similar algorithm. Experimental results show the advantages and disadvantages of the above three methods are made based on compared to other algorithms better GGO segmentation.

【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：R734.2;TP391.41

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