本文選題：CT圖像 + 肺結(jié)節(jié)��； 參考：《天津工業(yè)大學(xué)》2017年碩士論文

【摘要】：據(jù)世界各大癌癥協(xié)會(huì)報(bào)道,肺癌正逐步成為威脅人類健康的癌癥之首。肺結(jié)節(jié)是肺癌早期表現(xiàn)之一,鑒別肺結(jié)節(jié)的良惡性在肺結(jié)節(jié)患者的整個(gè)治療過(guò)程中占據(jù)極其重要的位置。本文提出的期望最大化法(Expectation Maximization Algorithm,EM)法能成功提取出肺結(jié)節(jié)中的實(shí)性成分,縮短了醫(yī)生手動(dòng)分割的時(shí)間,為醫(yī)生鑒別肺結(jié)節(jié)的良惡性提供一定的參考依據(jù)。亞實(shí)性肺結(jié)節(jié)惡性率較高,實(shí)性成分的大小可以作為判斷肺結(jié)節(jié)良惡性的一個(gè)參考,而目前針對(duì)CT圖像中亞實(shí)性肺結(jié)節(jié)實(shí)性成分的研究相對(duì)較少。針對(duì)這個(gè)問(wèn)題,本文共進(jìn)行了以下幾個(gè)方面的工作:分割出肺實(shí)質(zhì)圖像。本文在這一環(huán)節(jié)提出多次重復(fù)使用數(shù)學(xué)形態(tài)學(xué)運(yùn)算將相互粘連的左右肺區(qū)分開的方法,該方法運(yùn)算簡(jiǎn)單速度快。對(duì)所進(jìn)行實(shí)驗(yàn)的20個(gè)病例中含有肺結(jié)節(jié)的120張圖像,分割出肺實(shí)質(zhì)的準(zhǔn)確率為100%。在肺結(jié)節(jié)檢測(cè)的問(wèn)題上,本文共采用兩種方法,其一為Snake活動(dòng)輪廓法,其二為先使用模糊C均值聚類法檢測(cè)所有疑似肺結(jié)節(jié),由于直接得到的結(jié)果假陽(yáng)性肺結(jié)節(jié)較多,本文加入了對(duì)目標(biāo)形態(tài)特征的分析進(jìn)一步降低假陽(yáng)性率的方法,最終將肺結(jié)節(jié)分割出來(lái)。本文提出使用EM法提取肺結(jié)節(jié)實(shí)性成分,為了驗(yàn)證該方法的穩(wěn)定性,本文采用兩種方式分割肺結(jié)節(jié),并使用EM法對(duì)肺結(jié)節(jié)一一進(jìn)行實(shí)性成分的提取。得到的實(shí)性成分圖像與Philips viewer軟件中觀察的圖像做比較,探究亞實(shí)性肺結(jié)節(jié)實(shí)性成分提取的效果。通過(guò)對(duì)20個(gè)病例的實(shí)驗(yàn),結(jié)果表明,本文基于CT圖像采用EM法可以成功提取出亞實(shí)性肺結(jié)節(jié)中實(shí)性成分,結(jié)果與參考圖像十分接近,避免了因醫(yī)生手動(dòng)分割而帶來(lái)的不確定因素,很大程度上縮短了醫(yī)生分割實(shí)性成分的時(shí)間,減少其工作負(fù)擔(dān),并成功計(jì)算出實(shí)性成分的體積占據(jù)肺結(jié)節(jié)體積的比值,為醫(yī)生判定肺結(jié)節(jié)的良惡性提供重要的參考依據(jù)。
[Abstract]:Lung cancer is becoming the leading cancer threat to human health, according to the world's major cancer associations.Pulmonary nodules are one of the early manifestations of lung cancer. Differentiating benign and malignant pulmonary nodules plays an important role in the treatment of pulmonary nodules.The expectation maximization Maximization algorithm proposed in this paper can successfully extract the solid components from pulmonary nodules, shorten the time of manual segmentation by doctors, and provide a certain reference for doctors to distinguish benign and malignant pulmonary nodules.The malignant rate of subsolid pulmonary nodules is high and the size of solid components can be used as a reference for the diagnosis of benign and malignant pulmonary nodules.In order to solve this problem, we have done the following work: segmenting the lung parenchyma image.In this part, a method of dividing the conglutinated left and right lungs by repeated mathematical morphological operation is proposed, which is simple and fast.120 images of pulmonary nodules in 20 cases were used to segment pulmonary parenchyma with 100 accuracy.There are two methods to detect pulmonary nodules, one is Snake active contour method, the other is using fuzzy C-means clustering method to detect all suspected pulmonary nodules.In this paper, the method of reducing false positive rate is added to analyze the morphological characteristics of the target, and finally the pulmonary nodules are segmented.In this paper, EM method is used to extract the solid components of pulmonary nodules. In order to verify the stability of this method, two methods are used to segment pulmonary nodules, and EM method is used to extract the solid components of pulmonary nodules one by one.The solid component images obtained were compared with those observed in Philips viewer software to explore the effect of subsolid pulmonary nodule solid component extraction.The experimental results of 20 cases showed that the solid components of subsolid pulmonary nodules could be extracted successfully by using EM method based on CT images, and the results were very close to those of reference images.It avoids the uncertainty caused by the doctor's manual segmentation, greatly shortens the time for doctors to divide solid components, reduces their workload, and successfully calculates the ratio of the volume of solid components to the volume of pulmonary nodules.To provide an important reference for doctors to determine the benign and malignant pulmonary nodules.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R734.2;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 王繼魯;;多排螺旋CT診斷30例孤立性肺結(jié)節(jié)的結(jié)果分析[J];世界最新醫(yī)學(xué)信息文摘;2015年72期

2 劉士遠(yuǎn);;肺亞實(shí)性結(jié)節(jié)影像處理專家共識(shí)[J];中華放射學(xué)雜志;2015年04期

3 陳侃;李彬;田聯(lián)房;;基于模糊速度函數(shù)的活動(dòng)輪廓模型的肺結(jié)節(jié)分割[J];自動(dòng)化學(xué)報(bào);2013年08期

4 范立南;胡向麗;孫申申;;基于OTSU算法和帶通濾波器的毛玻璃型肺結(jié)節(jié)檢測(cè)[J];沈陽(yáng)大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年06期

5 韋春暉;;肺癌早期診斷進(jìn)展[J];臨床肺科雜志;2010年08期

6 楊基棟;;EM算法理論及其應(yīng)用[J];安慶師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2009年04期

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