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研究生:黃敏信
研究生(外文):Min-HsinHuang
論文名稱:胸部放射影像之氣管隆突偵測
論文名稱(外文):Carina Detection on Chest Radiography
指導教授:郭淑美郭淑美引用關係
指導教授(外文):Shu-Mei Guo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:醫學資訊研究所
學門:醫藥衛生學門
學類:醫學技術及檢驗學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:39
中文關鍵詞:氣管內管移動式胸腔放射影像氣管隆突數位影像處理
外文關鍵詞:endotracheal inbutationportable chest radiographycarinadigital image processing
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對於在加護病房使用呼吸器的病人,針對以移動式X光機所拍攝得的胸部放射影像,確認氣管內管與氣管隆突的相對位置是非常重要的。由於移動式胸部放射影像的對比低且雜訊多,氣管隆突的位置不容易被醫護人員清楚辨識。本論文提出ㄧ個嶄新的方法來解決此問題。此方法整合對比增強、影像切割與邊界萃取等多種數位影像處理技術,使用者由影像上原本就容易辨識的氣管上半部選取一參考區域,再根據此區域之資訊選擇閾值,分割出支氣管以下的區域,進而辨識出氣管隆突的位置。以加護病房實際之移動式胸腔放射影像進行試驗,證實提出之方法辨識出氣管隆突之成功率高達百分之九十二。以此方法為基礎,將來可進一步發展臨床警示系統,自動判讀胸部放射影像,針對氣管內管位置不適當之個案發出警訊,提醒醫謢人員儘早處理,改善病人安全。
It is important to check the position of the endotracheal tube on the portable chest radiography for patients in the intensive care units. The position of carina is not easy to identify on portable chest radiography due to the low image contrast and abundant noise at this region of interest. In this paper, a novel method is proposed to identify the position of carina. The proposed method is integrated with the rule-based image segmentation, contrast enhancement, selective thresholding, and morphological image processing. Experimental results show that the proposed method is robust and the success rate is 92.1%. It can be used to enhance patient safety by early detection and prompt correction of improper position of the endotracheal tube.
Abstract………………………………V
List of Tables……………IX
List of Figures…………X
Chapter 1 Introduction………1
1.1 The Importance of Proper Position of the Endotracheal Tube………………………………………………………1
1.2 Motivation……………………………3
1.3 Organization of the Thesis……5
Chapter 2 Background………………………………6
2.1 Computer-aided Detection and Diagnosis in Chest Radiography …………………………………………………6
2.1.1 The anatomical region of related study………………6
2.1.2 The material of related study………………………7
2.1.3 Review of computer-aided diction and diagnosis in portable chest radiography……………………………8
2.2 Image Features of the Carina in Chest Radiography………9
2.2.1 Anatomy of the airway system………………………9
2.2.2 Shape and location …………………………………9
2.2.3 Image density ………………………………………10
Chapter 3 Methodology ………………………11
3.1 Overview of Algorithm …………………………………11
3.2 Selection of ROI and Image Segmentation………………13
3.3 Contrast Enhancement …………………………………14
3.4 Selection of Crucial Sub-ROIs ………………………17
3.5 Image Binarization by Selective Thresholding………………19
3.6 Morphological Image Processing……………………………21
3.7 Identifying the Positioning of Carina………………………24
3.8 Algorithm of the Proposed Methodology………………………27
Chapter 4 Experimental Results……………………30
4.1 Data ………………………………………………………30
4.2 Performance………………………………………31
Chapter 5 Conclusions and Future Work…………35
Reference……………………………………………36

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