電機資訊學院: 生醫電子與資訊學研究所指導教授: 陳中平吳駿逸Wu, Chun-YiChun-YiWu2017-03-022018-07-052017-03-022018-07-052016http://ntur.lib.ntu.edu.tw//handle/246246/272591針對大腸鏡篩檢的影像，本論文提出了擷取盲腸與非盲腸之結構特徵的分析系統，利用特徵在兩者臨床照片中表現之不同，找出多維度的特徵向量，作為我們建立之分類系統的訓練資料，用以提升分類器之準確性和強健性，以及利用影像處理之結果，協助醫師判斷之目的。 近年來，人類的飲食習慣與生活與過去越來越不同，社會大眾對於自身的腸胃道系統也日益重視，消化系統相關的疾病儼然成為重要的課題，其中，又以大腸癌為國人罹患人數前三名以及高花費的病症。為了預防大腸癌以及維持良好的腸胃環境，國內外定期大腸鏡篩檢的需求量也不斷成長。然而為了達到有效的篩檢目的，完整的檢查程序是必須的，其中盲腸到達率(CIR)是檢查品質判斷一項非常重要的指標，故本論文注重於探討CIR以提升大腸鏡檢查的整體效益。我們提出一個系統架構，能夠基於影像處理的演算法，自動地辨識出影像中盲腸(Cecum)的結構表現，如回盲瓣Ileocecal Valve (ICV) 、微笑紋路Tri-radiate Fold、闌尾孔Appendiceal Orifice，另外，非盲腸部分的結構表現也是系統的擷取目標，並基於兩者的表現差異，找出多面向並且具有區別力的特徵，做為分類的依據。 本論文提出一套大腸鏡影像分析方法，包括有效地抑制如反光、血管、積水和糞便等雜訊，並找出適當的特徵值區別盲腸與非盲腸的結構表現，產生多維度特徵向量，提供整個大腸鏡影像分類系統的辨識。在此篇論文中，我們改良了先前研究單純針對單一亮度作為特徵擷取的演算法，提出Particle Filter將亮度特徵做一最佳化的擷取，另外，我們從Edge-Based、Histogram-Based 以及Texture-Based等不同方面進行大腸鏡影像的分析，包括Feature Extraction和Image Segmentation。最後我們另一位計畫夥伴將進一步以AdaBoost Machine Learning的方式進行特徵訓練，並得到一個將醫師上傳之大腸鏡影像分為盲腸或非盲腸之分類器。本論文提出的演算法，以臺大醫院提供之臨床影像，總共包含664位病人資料，我們隨機挑選出1009張盲腸與997張非盲腸影像作為訓練與測試，將結果與整體系統整合之後，最後我們得到了平均94.0%與最佳96.9%分類的準確率，並且基於這些特徵自動地標記出大腸鏡影像中之重要結構提供醫生作為判斷時之第三方參考。We proposed an analysis system to extract the features of cecum and non-cecum structures of clinical colonoscopy images. We found out the different expression of these two cases and obtained multiple features after extraction as the training data for our classifier of system. The proposed algorithm helped improve the accuracy and robustness compared to the previous work. Besides, we also provided assistance to doctors for their judgments with the result of image processing on colonoscopy images. In recent years, the dietary habit and life of people had become very different nowadays, and the concerns of the digestive system related diseases had become more and more serious issue to people. Among these, colorectal cancer has the high incident rate and high cost within the top ranks in our country. To prevent colorectal cancer and maintain good digestive environment, the demand of regular colonoscopy is increasing around the world. However, to achieve the purpose of the examination effectively, complete procedure is essential. The cecal intubation rate (CIR) is the critical quality indicator of colonoscopy completion, which is our system mainly focus on. We proposed an image processing based system, which could automatically recognize the landmarks of cecum images such as Ileocecal valve (ICV), Tri-radiate Fold and Appendiceal Orifice. In addition, we also found the structures of non-cecum images. And we determined the features to demonstrate the difference between these two cases performance for our classification accordance. We developed an approach to analyze colonoscopy images, including reducing the noises such as reflection, veins and dirt of excrements. Further, we extracted vector of multiple features to describe cecum and non-cecum structures’ performance properly for the classification and recognition in our system. In this thesis, we applied Particle Filter algorithm to have better result compared to previous work, which used merely single lightness threshold as the classification feature. Moreover, Edge-Based, Histogram-Based and Texture-Based are the additional aspects we utilized to extract our features for colonoscopy images analysis, including classification and segmentation. Finally, we pass our feature vector as training data to another partner in our project, who is focus on developing Machine Learning using Adaboost algorithm for the classification dealing with the photos uploaded by doctors. The material of our experiment was provided by NTU hospital, which were totally 664 patients’ folder of clinical colonoscopy images. We randomly selected from it and got 1009 cecum images and 997 non-cecum images for our training and testing purpose. At last, we not only got average 94.0% and best 96.9% accuracy of classification performance, but also marked the vital landmark of cecum and non-cecum structures for doctors’ analysis.3988133 bytesapplication/pdf論文公開時間: 2021/7/26論文使用權限: 同意有償授權(權利金給回饋本人)大腸鏡影像盲腸到達率影像處理影像辨識粒子濾波器紋理分析特徵擷取影像切割Colonoscopy ImageCecal Intubation RateImage ProcessingPattern RecognitionParticle FilterTexture AnalysisFeature ExtractionImage Segmentation[SDGs]SDG3thesis10.6342/NTU201600802http://ntur.lib.ntu.edu.tw/bitstream/246246/272591/1/ntu-105-R03945034-1.pdf