基于集成分類器和隨機(jī)森林算法的肺部CT圖像處理應(yīng)用研究
發(fā)布時(shí)間：2019-01-15 08:54
【摘要】：目的:近年來伴隨著霧霾天氣的不斷加重,間質(zhì)性肺病的患病率和死亡率持續(xù)上升,早期臨床癥狀不明顯,只有30%患者肺活檢診斷可以發(fā)現(xiàn)間質(zhì)性肺部疾病的癥狀。間質(zhì)性肺病雖發(fā)展至晚期較易診斷,但早已失去早期診斷的意義。肺部CT(Computed Tomography,CT)對肺組織和間質(zhì)更能細(xì)致顯示其形態(tài)結(jié)構(gòu)變化,尤其在判定間質(zhì)性肺病這類以肺部周邊病變?yōu)橹鞯姆尾考膊》矫婢哂歇?dú)特的診斷價(jià)值。在臨床輔助診斷中,由于肺實(shí)質(zhì)部分與其它氣管、支氣管等組織存在粘連,致使在確定病灶區(qū)域時(shí)存在一定的模糊信息。結(jié)合圖像預(yù)處理、特征提取、分類器等相關(guān)算法實(shí)現(xiàn)對肺部CT圖像的疾病分類以及肺實(shí)質(zhì)分割,為疾病的診斷提供更多更加有效的病灶信息,有利于間質(zhì)性肺病后續(xù)的治療。方法:本研究針對不同類型肺部疾病在CT圖像上相似病理特征,通過PHOG(Pyramid Histogram of Oriented Gradients,PHOG)算法提取肺部CT圖像的方向梯度信息,采取“投票”的思想訓(xùn)練基分類器成為集成分類器,構(gòu)建一個(gè)魯棒的分類模型,進(jìn)而對冗雜的肺部CT圖像實(shí)現(xiàn)患病和健康兩種不同表征類型的有效區(qū)分。其次,課題研究針對肺部CT圖像肺實(shí)質(zhì)與非肺實(shí)質(zhì)部位存在不清晰邊界的問題,利用肺部CT圖像紋理變化大,灰度對比明顯的特征,運(yùn)用灰度共生矩陣算法獲取紋理特征同時(shí)融合灰度特征構(gòu)成特征矩陣,選取隨機(jī)森林作為分類器,提出一種超像素與隨機(jī)森林復(fù)合的分割算法,實(shí)現(xiàn)肺實(shí)質(zhì)的準(zhǔn)確分割。結(jié)果:為測試算法模型的性能選取日內(nèi)瓦大學(xué)一個(gè)公開的間質(zhì)性肺病的數(shù)據(jù)庫ILDs(Interstitial Lung Diseases)。實(shí)驗(yàn)結(jié)果表明,基于集成分類器算法的肺部CT圖像疾病分類模型的準(zhǔn)確率達(dá)到94.55%,敏感度為86.44%,取得比較理想的分類結(jié)果;基于隨機(jī)森林的肺部CT圖像分割算法在健康肺部CT圖像的準(zhǔn)確率高達(dá)99.09%,肺部纖維化、毛玻璃、肺氣腫、肺結(jié)節(jié)患病圖像分割準(zhǔn)確率均在90%以上。結(jié)論:在基于肺部CT圖像疾病分類方面,本文提出的基于集成分類器的分類模型,能夠高精度的實(shí)現(xiàn)肺部CT圖像健康和患病兩種類型的分類,且魯棒性較好。肺部CT圖像分類算法模型敏感度雖還有待進(jìn)一步提高,但是對于肺病的臨床診斷治療方案的確定具有一定的現(xiàn)實(shí)意義。在肺實(shí)質(zhì)分割方面,提出的基于隨機(jī)森林分類器的方法,能夠準(zhǔn)確高效的實(shí)現(xiàn)不同種類病理表征肺部CT圖像的肺實(shí)質(zhì)部分的分割。在患病嚴(yán)重的肺部CT圖像的分割和算法的運(yùn)算效率還需進(jìn)一步研究,其對于開展肺部CT圖像的檢測、量化等進(jìn)一步的工作仍然具有很好的應(yīng)用前景。
[Abstract]:Objective: in recent years, with the worsening of haze weather, the morbidity and mortality of interstitial pulmonary disease are rising, and the early clinical symptoms are not obvious. Only 30% of the patients can find the symptoms of interstitial lung disease by lung biopsy diagnosis. Although it is easy to diagnose interstitial pulmonary disease at late stage, it has long lost the significance of early diagnosis. Pulmonary CT (Computed Tomography,CT) can show the morphologic and structural changes of lung tissue and interstitial more carefully, especially in the diagnosis of interstitial pulmonary disease, which is dominated by pulmonary peripheral lesions. In clinical assistant diagnosis, there is some fuzzy information in the determination of lesion area due to the adhesion of lung parenchyma with other trachea and bronchi. Combined with image preprocessing, feature extraction, classifier and other related algorithms to achieve lung CT image disease classification and lung parenchyma segmentation, for the diagnosis of disease to provide more effective focus information, conducive to the follow-up treatment of interstitial pulmonary disease. Methods: based on the similar pathological features of different types of lung diseases on CT images, the directional gradient information of lung CT images was extracted by PHOG (Pyramid Histogram of Oriented Gradients,PHOG algorithm. The idea of "vote" is adopted to train the base classifier as an integrated classifier, and a robust classification model is constructed, which can effectively distinguish ill and healthy lung CT images. Secondly, aiming at the problem of unclear boundary between lung parenchyma and non-pulmonary parenchyma in lung CT images, the feature of large texture change and obvious grayscale contrast of lung CT image is used. Using gray level co-occurrence matrix algorithm to obtain texture features and fuse gray features to form feature matrix, select random forest as classifier, propose a segmentation algorithm which is composed of hyperpixel and random forest, and realize accurate segmentation of lung parenchyma. Results: to test the performance of the algorithm model, select ILDs (Interstitial Lung Diseases)., an open database of interstitial pulmonary diseases at the University of Geneva. The experimental results show that the disease classification model of lung CT image based on the integrated classifier algorithm has the accuracy of 94.555.The sensitivity is 86.44. The accuracy rate of lung CT image segmentation based on random forest is 99.09% in healthy lung CT image. The segmentation accuracy of pulmonary fibrosis, glass, emphysema and pulmonary nodule disease image is over 90%. Conclusion: in the aspect of disease classification based on lung CT image, the classification model based on integrated classifier can achieve the classification of lung CT image health and disease with high accuracy and good robustness. Although the sensitivity of lung CT image classification algorithm model needs to be further improved, it has a certain practical significance for the clinical diagnosis and treatment of lung disease. In the aspect of lung parenchyma segmentation, the proposed method based on stochastic forest classifier can accurately and efficiently segment the lung parenchyma of different kinds of pathological CT images. The segmentation of lung CT images and the computational efficiency of the algorithm need to be further studied, which still has a good application prospect for the detection and quantification of lung CT images.
【學(xué)位授予單位】：山東中醫(yī)藥大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R816.4;TP391.41
本文編號(hào)：2409055
[Abstract]:Objective: in recent years, with the worsening of haze weather, the morbidity and mortality of interstitial pulmonary disease are rising, and the early clinical symptoms are not obvious. Only 30% of the patients can find the symptoms of interstitial lung disease by lung biopsy diagnosis. Although it is easy to diagnose interstitial pulmonary disease at late stage, it has long lost the significance of early diagnosis. Pulmonary CT (Computed Tomography,CT) can show the morphologic and structural changes of lung tissue and interstitial more carefully, especially in the diagnosis of interstitial pulmonary disease, which is dominated by pulmonary peripheral lesions. In clinical assistant diagnosis, there is some fuzzy information in the determination of lesion area due to the adhesion of lung parenchyma with other trachea and bronchi. Combined with image preprocessing, feature extraction, classifier and other related algorithms to achieve lung CT image disease classification and lung parenchyma segmentation, for the diagnosis of disease to provide more effective focus information, conducive to the follow-up treatment of interstitial pulmonary disease. Methods: based on the similar pathological features of different types of lung diseases on CT images, the directional gradient information of lung CT images was extracted by PHOG (Pyramid Histogram of Oriented Gradients,PHOG algorithm. The idea of "vote" is adopted to train the base classifier as an integrated classifier, and a robust classification model is constructed, which can effectively distinguish ill and healthy lung CT images. Secondly, aiming at the problem of unclear boundary between lung parenchyma and non-pulmonary parenchyma in lung CT images, the feature of large texture change and obvious grayscale contrast of lung CT image is used. Using gray level co-occurrence matrix algorithm to obtain texture features and fuse gray features to form feature matrix, select random forest as classifier, propose a segmentation algorithm which is composed of hyperpixel and random forest, and realize accurate segmentation of lung parenchyma. Results: to test the performance of the algorithm model, select ILDs (Interstitial Lung Diseases)., an open database of interstitial pulmonary diseases at the University of Geneva. The experimental results show that the disease classification model of lung CT image based on the integrated classifier algorithm has the accuracy of 94.555.The sensitivity is 86.44. The accuracy rate of lung CT image segmentation based on random forest is 99.09% in healthy lung CT image. The segmentation accuracy of pulmonary fibrosis, glass, emphysema and pulmonary nodule disease image is over 90%. Conclusion: in the aspect of disease classification based on lung CT image, the classification model based on integrated classifier can achieve the classification of lung CT image health and disease with high accuracy and good robustness. Although the sensitivity of lung CT image classification algorithm model needs to be further improved, it has a certain practical significance for the clinical diagnosis and treatment of lung disease. In the aspect of lung parenchyma segmentation, the proposed method based on stochastic forest classifier can accurately and efficiently segment the lung parenchyma of different kinds of pathological CT images. The segmentation of lung CT images and the computational efficiency of the algorithm need to be further studied, which still has a good application prospect for the detection and quantification of lung CT images.
【學(xué)位授予單位】：山東中醫(yī)藥大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R816.4;TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前2條
1 劉靜;郭建;賀遵亮;;基于Gist和PHOG特征的場景分類[J];計(jì)算機(jī)工程;2015年04期
2 龔敬;王麗嘉;王遠(yuǎn)軍;孫希文;聶生東;;基于灰度積分投影與模糊C均值聚類的肺實(shí)質(zhì)分割[J];中國生物醫(yī)學(xué)工程學(xué)報(bào);2015年01期
，本文編號(hào)：2409055
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