本文關(guān)鍵詞：基于低劑量CT圖像序列的三維肺實(shí)質(zhì)提取　出處：《鄭州大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 低劑量CT 肺實(shí)質(zhì) 圖像序列 陡變度 自動(dòng)化 三維分割

【摘要】：肺癌是患病率和病死率最高的惡性腫瘤。隨著近年空氣污染和霧霾越來越嚴(yán)重,開展肺癌的早期篩查愈來愈緊迫。與X線胸片相比,采用低劑量CT(low-dose computed tomography,LDCT)對(duì)肺癌高危人群進(jìn)行篩查可使肺癌病死率下降20%。由此可以預(yù)見,基于低劑量螺旋CT取代X線胸片進(jìn)行肺部疾病篩查是未來發(fā)展的趨勢(shì)。肺癌早期一般以肺結(jié)節(jié)的形式出現(xiàn),而肺實(shí)質(zhì)分割是肺結(jié)節(jié)檢測(cè)的重要前提。鑒于此,本文基于低劑量CT圖像序列,研究精準(zhǔn)的三維肺實(shí)質(zhì)提取算法。本文的主要工作如下:(1)精準(zhǔn)的二維肺實(shí)質(zhì)分割算法針對(duì)噪聲、肺部區(qū)域的不均勻性以及胸膜與肺結(jié)節(jié)的粘連會(huì)影響二維肺實(shí)質(zhì)分割精度的問題,提出精準(zhǔn)的二維肺實(shí)質(zhì)分割算法。首先,采用傳統(tǒng)的肺實(shí)質(zhì)分割方法,經(jīng)過預(yù)處理、二值化、去除氣管和支氣管以及左右肺分離步驟,初步分割出肺實(shí)質(zhì)區(qū)域;然后針對(duì)肺實(shí)質(zhì)初步分割中近胸膜結(jié)節(jié)與血管易被錯(cuò)誤排除在肺實(shí)質(zhì)區(qū)域之外的問題,提出了一種基于陡變度的肺實(shí)質(zhì)邊緣修補(bǔ)算法,通過檢測(cè)肺實(shí)質(zhì)邊緣的陡變點(diǎn)、提取肺實(shí)質(zhì)邊緣的拐角點(diǎn),以及選取并連接重要拐角點(diǎn)對(duì),即可準(zhǔn)確修補(bǔ)肺實(shí)質(zhì)邊緣凹陷,進(jìn)而得到完整的肺實(shí)質(zhì)圖像。該算法能精確檢測(cè)肺實(shí)質(zhì)邊緣的拐角點(diǎn),進(jìn)而能高效修補(bǔ)肺實(shí)質(zhì)邊緣凹陷。與文獻(xiàn)法相比,提出算法的二維肺實(shí)質(zhì)分割精度提升了0.76%,但分割速度較低,需進(jìn)一步改進(jìn)。(2)快速的低劑量CT圖像序列自動(dòng)化分割針對(duì)循環(huán)處理低劑量CT圖像序列中的二維切片需消耗大量時(shí)間與人力的問題,提出一種改進(jìn)的基于兩級(jí)隊(duì)列的3D并行區(qū)域生長(zhǎng)方法,以快速實(shí)現(xiàn)圖像序列分割的自動(dòng)化。該算法在傳統(tǒng)區(qū)域生長(zhǎng)法的基礎(chǔ)上,采用兩級(jí)隊(duì)列進(jìn)行生長(zhǎng),并優(yōu)先搜索邊緣鄰域點(diǎn),加快了生長(zhǎng)速度;同時(shí)詳細(xì)考慮了種子點(diǎn)在相鄰切片之間的變化情況,進(jìn)而可實(shí)現(xiàn)對(duì)圖像序列的自動(dòng)化分割,并避免了分割錯(cuò)誤。與傳統(tǒng)區(qū)域生長(zhǎng)法相比,提出算法的單幅圖像平均處理時(shí)間減少了0.35s,體積重疊率提升了0.15%,過分割率降低了0.07%,欠分割率降低了0.04%。與文獻(xiàn)法相比,體積重疊率提升了1.03%,過分割率降低了0.08%,欠分割率降低了1.2%,近胸膜結(jié)節(jié)包含率高達(dá)100%,單幅圖像平均處理時(shí)間下降了0.09s。由此證明,提出算法能高效實(shí)現(xiàn)低劑量CT圖像序列分割的自動(dòng)化,為三維肺實(shí)質(zhì)分割提供了便利條件。(3)三維肺實(shí)質(zhì)提取最后,采用本文方法對(duì)多組低劑量CT圖像序列進(jìn)行處理,并對(duì)分割出的肺實(shí)質(zhì)圖像序列進(jìn)行三維重建,以提取三維肺實(shí)質(zhì)圖像。實(shí)驗(yàn)結(jié)果表明,本文方法不僅能快速分割出高精度的肺實(shí)質(zhì)圖像序列,并且具有很好的三維可視化效果。
[Abstract]:Lung cancer is the malignant tumor with the highest morbidity and mortality. With the air pollution and haze becoming more and more serious in recent years, it is more and more urgent to carry out early screening of lung cancer. Low dose CT(low-dose computed tomography was used. LDCT screening of high risk groups of lung cancer can reduce the mortality of lung cancer by 20%, which can be predicted. Lung disease screening based on low dose spiral CT instead of chest radiography is a trend in the future. Lung cancer usually occurs in the form of pulmonary nodules in the early stage, and pulmonary parenchyma segmentation is an important prerequisite for the detection of pulmonary nodules. Based on the low dose CT image sequence, this paper studies a precise three-dimensional lung parenchyma extraction algorithm. The main work of this paper is as follows: 1) the precise two-dimensional lung parenchyma segmentation algorithm is aimed at noise. The inhomogeneity of lung region and the adhesion between pleura and pulmonary nodules will affect the segmentation accuracy of two-dimensional lung parenchyma. A precise two-dimensional segmentation algorithm of lung parenchyma is proposed. Firstly, the traditional method of lung parenchyma segmentation is adopted. After pretreatment, binarization, trachea and bronchus removal, and left and right lung separation steps, the lung parenchyma area was preliminarily separated. Then, aiming at the problem that the near pleural nodules and blood vessels are easily misruled out of the lung parenchyma region in the primary segmentation of pulmonary parenchyma, an algorithm of pulmonary parenchyma edge repair based on the degree of steepness is proposed. By detecting the sharp change point of the pulmonary parenchyma edge, extracting the corner point of the pulmonary parenchyma edge, and selecting and connecting the important corner pair, the indentation of the pulmonary parenchyma edge can be repaired accurately. The algorithm can accurately detect the corner point of the pulmonary parenchyma edge, and then it can effectively repair the indentation of the pulmonary parenchyma edge, compared with the literature method. The segmentation accuracy of two-dimensional lung parenchyma is improved by 0.76, but the segmentation speed is low. The fast automatic segmentation of low dose CT image sequence is needed to solve the problem that it takes a lot of time and manpower to process 2D slice in low dose CT image sequence. An improved 3D parallel region growth method based on two-level queue is proposed to automate image sequence segmentation. The algorithm is based on the traditional region growth method and uses two-level queue to grow. And priority search edge neighborhood points, accelerate the growth rate; At the same time, the variation of seed points between adjacent slices is considered in detail, which can realize the automatic segmentation of image sequences and avoid segmentation errors, compared with the traditional region growth method. The proposed algorithm reduces the average processing time of a single image by 0.35s, increases the volume overlap rate by 0.15s, and reduces the over-segmentation rate by 0.07%. Compared with the literature method, the volume overlap rate increased by 1.033%, the over-segmentation rate decreased by 0.08%, and the under-segmentation rate decreased by 1.2%. The inclusion rate of near-pleural nodules is as high as 100 and the average processing time of a single image is decreased by 0.09s. It is proved that the proposed algorithm can efficiently automate the segmentation of low-dose CT images. Three dimensional lung parenchyma segmentation provides a convenient condition for three-dimensional lung parenchyma extraction. Finally, we use this method to process multi-group low-dose CT image sequence. The segmented lung parenchyma image sequence is reconstructed to extract the three-dimensional lung parenchyma image. The experimental results show that the proposed method can not only segment the high-precision lung parenchyma image sequence quickly. And has the very good three-dimensional visualization effect.
【學(xué)位授予單位】：鄭州大學(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

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本文編號(hào)：1399323