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.

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