【摘要】：背景：在現(xiàn)代社會(huì)中,隨著工業(yè)化的發(fā)展,導(dǎo)致空氣污染加重,人體肺部疾病發(fā)病率高,類型眾多,如肺癌,氣管炎,肺氣腫,哮喘等都會(huì)嚴(yán)重危害人體健康。隨著多排螺旋CT的發(fā)展,醫(yī)生可以通過(guò)一次掃描便獲得患者的影像信息,并結(jié)合先進(jìn)的影像處理技術(shù)對(duì)疾病進(jìn)行分析診斷�；谏鲜鰞�(yōu)點(diǎn),CT成像并認(rèn)為是診斷肺部疾病的金標(biāo)準(zhǔn)。但由于氣管樹(shù)的結(jié)構(gòu)復(fù)雜,對(duì)比度差,并且容易受到噪聲、容積效應(yīng)的影響,如何準(zhǔn)確的在CT數(shù)據(jù)中提取完整的氣管樹(shù)結(jié)構(gòu)仍面臨挑戰(zhàn)。 目的：基于人體氣管樹(shù)的形態(tài)學(xué)結(jié)構(gòu),提出一種全新的方法來(lái)提取氣管樹(shù)結(jié)構(gòu),并為臨床診斷提供參考依據(jù)。材料和方法：本課題采用的所有CT數(shù)據(jù)全部來(lái)自遼寧省沈陽(yáng)市盛京醫(yī)院,所有的CT圖像的重建矩陣大小皆為512*512,機(jī)器型號(hào)包括飛利浦Brilliance64和東芝。本算法首先基于區(qū)域生長(zhǎng),波傳遞算法,形態(tài)優(yōu)化和填洞操作來(lái)提取主氣管。再提取肺區(qū)其余部位的低CT值區(qū)域,并使用一些形態(tài)學(xué)手段來(lái)得到肺區(qū)內(nèi)的管狀結(jié)構(gòu)特征組織,之后篩選正確的遺漏氣管并將其和主氣管進(jìn)行拼接,最后再通過(guò)迭代操作來(lái)得到氣管分割的最佳結(jié)果。 結(jié)果：此算法已在成功應(yīng)用于28個(gè)肺部CT數(shù)據(jù),并邀請(qǐng)盛京醫(yī)院的放射科醫(yī)生對(duì)分割結(jié)果進(jìn)行評(píng)估,并與人體實(shí)際支氣管的解剖學(xué)特征進(jìn)行對(duì)比,根據(jù)統(tǒng)計(jì)結(jié)果可得本算法能夠分割出5級(jí)以上的人體支氣管。 結(jié)論：人體氣管樹(shù)的分割結(jié)果能為醫(yī)生提供一定的臨床參考,并為氣管分支分段骨架化和氣管仿真內(nèi)窺鏡提供研究基礎(chǔ)。
[Abstract]:Background: in modern society, with the development of industrialization, air pollution aggravates, the incidence of human lung diseases is high, many types, such as lung cancer, tracheitis, emphysema, asthma and so on, will seriously endanger human health. With the development of multi-slice spiral CT, doctors can obtain the patient's image information through one scan, and combine with advanced image processing technology to analyze and diagnose the disease. Based on these advantages, CT imaging is considered to be a gold standard for the diagnosis of pulmonary diseases. However, because the structure of trachea tree is complex, the contrast is poor, and it is easy to be affected by noise and volume effect, how to accurately extract the complete trachea tree structure from CT data is still a challenge. Aim: based on the morphological structure of human trachea tree, a new method was proposed to extract trachea tree structure and provide reference for clinical diagnosis. Materials and methods: all the CT data used in this study are from Shengjing Hospital, Shenyang, Liaoning Province. The reconstruction matrix size of all CT images is 512 / 512. The machine models include Philips Brilliance64 and Toshiba. Firstly, the algorithm is based on region growth, wave transfer algorithm, shape optimization and hole filling operation to extract the main trachea. Then the low CT value region of the rest of the lung area was extracted, and some morphological methods were used to obtain the tubular structure characteristic tissue in the lung area, and then the correct missing trachea was screened and spliced with the main trachea. Finally, the optimal results of trachea segmentation are obtained by iterative operation. Results: this algorithm has been successfully applied to 28 lung CT data. Radiologists in Shengjing Hospital are invited to evaluate the segmentation results and compare them with the anatomical characteristics of the actual bronchus in human body. According to the statistical results, the algorithm can be used to segment human bronchi at or above level 5. Conclusion: the segmentation results of human trachea tree can provide a certain clinical reference for doctors, and provide a basis for the research of segmental ossification of trachea branches and tracheal virtual endoscopy.
【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】：2012
【分類號(hào)】：R816.41;TP391.41

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