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研究生:謝佳彣
研究生(外文):Chia-Wen Hsieh
論文名稱:應用類神經網路於肺部電腦斷層掃描之肺結節自動偵測與分類及三維重建與體積量測之系統開發與研究
論文名稱(外文):Using Artificial Neural Network for Automatic Detection and Classification of Pulmonary Nodules in Lung CT and 3D Reconstruction and Volume Measurement
指導教授:郭中豐郭中豐引用關係
指導教授(外文):Chung-Feng Jeffrey Kuo
口試委員:徐先和黃昌群高志遠
口試委員(外文):Hsian-He HsuChang-Chiun HuangChih-yuan Kao
口試日期:2019-01-27
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:材料科學與工程系
學門:工程學門
學類:材料工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:104
中文關鍵詞:肺結節三維形態特徵灰階值層級差分方法類神經網路三維體積重建
外文關鍵詞:Pulmonary nodulethree-dimensional morphological featureGray Level Difference Methodneural networksMarching cube
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肺癌死亡率為所有癌症最高,若能及早正確檢查出肺結節及其種類,可提高患者的治療效果。臨床上肺結節篩查是經由醫師判斷,以尺寸外觀資訊找尋肺結節和評估其嚴重性。由於肺結節具有不同大小及形態,早期診斷時在影像中有時不易發現。因此,本研究開發可自動偵測與分類肺部電腦斷層掃描影像肺結節之電腦輔助診斷系統,能同時偵測與分類固體、部分固體與毛玻璃狀陰影結節,並於CT影像中圈選其位置,最後三維重建各類肺結節的型態與計算體積,以輔助臨床醫師確保能直接觀察且快速得知肺結節資訊,提高診療效果。
本研究分為兩部分,第一部分為肺結節的篩選與分類,首先針對肺葉影像進行亮度調整,使三種肺結節之灰階亮度能統一範圍,為篩選肺結節,以灰階閥值下限法濾除非肺結節之物件,根據肺結節二維與三維形態,選取七種特徵值進行類神經網路的訓練與測試,篩選結果達到93.13%的靈敏度。再根據三種肺結節之灰階值與紋理特性,選取灰階值層級差分方法特徵進行肺結節分類,達到92.70%之靈敏度,驗證系統能輔助臨床醫師減少肺部篩查結節之發生遺漏,並能快速得知結節種類,正確對患者之病情進行診斷。
第二部分為進行肺結節之三維形態、體積、RECIST與結節最大橫截面積的數據計算,先以肺結節區塊之平均灰階標準差與影像熵值提取所有含有肺結節之切片,而後以適應性對比度拉伸強化肺結節影像,再針對三種肺結節進行不同之形態學處理,提取肺結節每個切片的橫截面積,以達到自動化提取整體肺結節之的目的,之後運用歐式距離公式計算其RECIST值以及統計最大橫截面積的像素個數進行面積計算,最後應用Marching cube演算法與黎曼積分公式求得結節體積。本研究以仿肺結節型態之黏土模型為實體樣本並重複拍攝電腦斷層影像,實體體積與經由電腦斷層影像求取之體積兩者之誤差平均為0.37%,驗證本研究所求得電腦斷層影像之體積的可靠度與重複性之結果。
Lung cancer has the highest mortality among all cancers, and if the pulmonary nodules and categories can be detected correctly and early, then the treatment effect on the patient can be enhanced. Clinically, the pulmonary nodules are screened according to the doctor's diagnosis, whereby they are searched for and their severity is evaluated according to size and appearance. As pulmonary nodules have different sizes and forms, they are sometimes unlikely to be detected in the image during early diagnosis. Therefore, this study develops a computer-aided diagnosis system for automatic detection and classification of pulmonary nodules in the lung computed tomography (CT) image, which can simultaneously detect and classify ground glass opacity (GGO), part solid, and solid nodule. The various pulmonary nodules are reconstructed and the corresponding volumes are calculated, so as to assist the physician to observe and obtain the pulmonary nodule information directly and rapidly. The end result is enhancing the diagnosis and treatment effects.
This study is divided into two parts. Part I deals with the screening and classification of pulmonary nodules. The brightness of the image of lung lobes is adjusted first, so as to unify the range of the gray level brightness of two kinds of pulmonary nodules. In order to screen pulmonary nodules, the objects of non-pulmonary nodules are filtered out by the Gray-scale threshold lower limitation method. According to the two-dimensional and three-dimensional forms of pulmonary nodules, seven eigenvalues are selected for Artificial neural network training and testing. The sensitivity of the screening result is 93.13%. Based on the texture features of the three kinds of pulmonary nodules, the Gray level difference method feature is selected for classification of pulmonary nodules. The sensitivity of the classification is 92.70%, proving that the system can assist clinicians to effectively reduce omissions in pulmonary nodule screening. The nodule category is obtained rapidly, so as to help the physician to diagnose the patient's condition correctly.
Part II obtains the three-dimensional morphology, volume, Response evaluation criteria in solid tumor (RECIST), and maximum cross-sectional area of the pulmonary nodules. First, all of the slices containing pulmonary nodules are extracted from the average gray-scale standard deviation and entropy from the image of the pulmonary nodule region. The pulmonary nodule image is enhanced by adaptive contrast stretch, and the morphologies of the pulmonary nodules are processed. The cross-sectional area of each slice of pulmonary nodule is extracted, so as to get the whole pulmonary nodule automatically. The RECIST value is next calculated by using the Euclidean distance formula, and the number of pixels of maximum cross-sectional area is counted for area computation. Finally, the nodule volume is obtained by using the Marching cube algorithm and Riemann integral formula. This study uses the clay model imitating pulmonary nodule morphology as physical samples. Each physical sample takes a CT three times. The average error between the physical volume and the volume derived from this study is 0.37%, thus proving the volume reliability and repeatability of CT from our proposed method.
摘要 III
Abstract V
致謝 VII
目錄 VIII
圖目錄 XIII
表目錄 XVI
第1章 緒論 1
1.1研究背景與動機 1
1.2文獻回顧 3
1.2.1 肺部分割 3
1.2.2 輪廓提取 5
1.2.3電腦輔助診斷系統 6
1.2.4病灶的定量評估 7
1.3研究目的 9
1.4論文架構 10
第2章 肺部相關醫學介紹 13
2.1肺部構造與功能 13
2.2肺結節 14
2.2.1固體結節 14
2.2.2部分固體結節 15
2.2.3毛玻璃狀陰影結節 15
2.3肺結節的醫學治療建議指標 16
2.3.1固體結節 18
2.3.2毛玻璃狀陰影結節 18
2.3.3部分固體結節 19
2.4肺結節的定量評估方法 20
第3章 醫學影像擷取系統與系統環境 22
3.1醫學影像擷取系統 22
3.2系統環境 25
3.3研究樣本 26
第4章 研究方法與理論 28
4.1影像前處理 28
4.1.1中值濾波器 28
4.1.2自適應直方圖等化法 29
4.2二值化法 32
4.2.1 Otsu法 32
4.2.2灰階值閥值下限法 35
4.3形態學 35
4.3.1侵蝕與膨脹 36
4.3.2開運算與閉運算 38
4.3.3 連通標記 40
4.3.4 區域填充 41
4.4直方圖平移法 42
4.5輪廓增強理論 43
4.5.1線性對比度拉伸 43
4.5.2適應性對比度拉伸 44
4.6影像特徵值 45
4.6.1篩選結節之形態特徵值 46
4.6.2分類結節之紋理特徵值 49
4.6.3含有結節的切片特徵值 54
4.7人工神經網路分類器 54
4.8三維重建 57
4.9結節資訊數據計算 60
4.9.1結節一維量測 60
4.9.2結節二維量測 61
4.9.3結節三維量測 61
第5章 系統結果與分析驗證 63
5.1影像前處理 63
5.2肺葉區塊提取 64
5.2.1肺部外圍輪廓遮罩 64
5.2.2肺葉區塊提取 65
5.3肺結節篩選 66
5.3.1濾除過低像素值之候選點 67
5.3.2肺結節篩選之結果 68
5.4肺結節分類 70
5.6.1結節區塊提取與前處理 70
5.6.2灰階值層級差分方法 71
5.5三維重建 74
5.5.1提取肺結節 74
5.5.2邊界提取 76
5.5.3 三維肺結節 78
5.6系統結果與比較 79
5.6.1系統執行結果 80
5.6.2與其他系統之比較 85
5.7體積驗證與分析 87
第6章 結論 95
參考文獻 97
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