- 學位論文
肺部電腦斷層掃描三維影像之小腫瘤分析及判別
Classification and Determination for Small Nodules in 3-D Pulmonary CT Image
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摘要
根據衛生署統計肺癌則已連續二十多年居於男性惡性腫瘤死因之第二位與女性惡性腫瘤死因之第一位。由於肺癌的末期治癒率相當的低，故如何在早期就診斷出肺癌是相當重要的議題。 低劑量電腦斷層掃描已有能力偵測到直徑1-2 mm的肺部腫瘤，但是肺部電腦斷層掃描的判讀卻不是一件容易的事。例如，對於分辨圓形的腫瘤與管狀的血管結構，醫師必須逐張切片影像仔細檢查，這是非常耗費時間且極為容易造成判斷錯誤。尤其是在電腦斷層影像中尋找微小肺腫瘤更是容易造成失誤。如果醫師僅以肉眼尋找，其準確程度有其極限。 而電腦輔助偵測不僅在研究上不斷被證明可以協助醫師提高電腦斷層掃描中肺腫瘤的偵測率，在醫療儀器市場上所逐漸受到重視。然而，不論是商用產品的電腦輔助偵測軟體或是仍處於實驗室階段的電腦輔助偵測與診斷軟體，都未臻理想，存在著很大的進步空間。故如何提升電腦輔助偵測軟體的效能，為一重要研究課題。 本研究針對肺部電腦斷層掃描影像，對在肺臟中的小腫瘤做了一系列的探討及分析，並提出一套方法希望電腦能透過這些資料自我學習如何辨識腫瘤，以期盼在未來協助醫師判斷及診斷。本研究所採用的肺部電腦斷層影像為美國國家癌症研究會(NATIONAL CANCER INSTITUTE，NCI)所提供的397位病人的電腦斷層掃描影像以及四位醫師在上面的標記。 本研究著重於小腫瘤的分析及判別，其流程分為三個部份，第一步先將肺區透過自適應的閥值及型態學的方法分割出來。再使用Hessian Matrix找出血管的樹狀結構並將血管標示出來。在有了肺區及血管資訊之後，將小腫瘤附近的肺壁及血管的資料連帶小腫瘤本身的資訊紀錄下來，以供後續分析。第二步將紀錄下來的資料依照四種不同類型的腫瘤做自動化分割，再對血管及肺壁附近的腫瘤做分割後的微調，使分割的結果更加精確。由分割後的結果，依據腫瘤本身的特徵及腫瘤附近的特徵做一系列的分析。第三步是由這些特徵資料訓練一個自動化的分類器學習如何辨識小腫瘤，經過基因演算法做特徵分類訓練分類器，再由學習後的分類器分類腫瘤。 本研究使用397位病人的肺部影像資料，將資料隨機抽選為不重複的訓練樣本及測試樣本。經過訓練樣本所訓練出的分類器，對於所抽選的測試樣本可達到平均83%以上的正確率。
並列摘要
Statistics from health department shows that the lung cancer has been on the second place for male and the first place for female in causes of death by malignant tumors for more than twenty consecutive years. Since the survival rate of late stage lung cancer is relatively low, early diagnosis is critical to survival. Low dose CT is capable of detecting lung tumors of length 1-2 mm in diameter, however the CT interpreting of which remains as a non-trivial task. For example, to identify round-shape tumor and tube-shape blood vessel structure, a physician must undergo a time-consuming and error-prone process to examine each CT carefully. And it is even more difficult when it comes to find small nodules in this way. A physician must go beyond naked-eyes examination for better precision and efficiency. Computer aid has been proven to be able to assist physicians in improving detection rate of lung tumors in CT scans, and it is getting wider popularity in the business of medical instruments. However, detection software in either commercial products or laboratory developments has yet much room to improve. Therefore it is an essential subject of research to improve the performance of computer aided detection software. This research goes through a series of analysis on pulmonary nodules and presents a set of methods for computers to be able to learn identifying tumors, and it is hoped that these methods may serve as diagnosis aids for doctors in the future. NATIONAL CANCER INSTITUTE (NCI) provides essential data including computed tomography (CT) images of around 397 patients with doctors' marking. The first step of the research is to automatically identify nodules by computer and store the data of nodules and nearby lung wall and blood vessels. The second step is to automatically partition the data by the types of nodules and perform a series of characteristic analysis. The third step is to train a automatic operator that classifies tumors on the basis of the characteristic data obtained in previous step, and the trained operator is put to work identifying nodules and then be evaluated for performance. With the tumor data of 397 patients as the basis of training, the automatic operator achieves a satisfactory performance. The classifier trained out of the samples can achieve 83% up correction rate on average on the selected samples.
參考文獻
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