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研究生:邱威智
研究生(外文):Wei-ChihChiu
論文名稱:以查表方式加速演算式計算運用於牙科放射性像差攝影術前校正
論文名稱(外文):A Look-up Table Strategy to Hasten Evolutionary Computing in Registration of Dental Subtraction 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:48
中文關鍵詞:牙科像差放射性影像基因演算法查表方式快速校正
外文關鍵詞:Dental subtraction radiographygenetic algorithmlookup table strategyquickly registration
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目前牙科像差放射性影像的取得,多依靠牙齒印模來固定照相角度關係,以取得具有相似對應之幾何關係的影像。因為牙齒印模裝置保存,消毒滅菌不便,無法用於實際臨床之上。本論文提出一套牙科X光片影像校正之方式,能迅速地達成牙科放射性像差攝影術的實際臨床運用。本文提出的系統分為兩個階段並分別結合兩種不同的進化演算方法,以達到加速運算的效果。第一階段先利用基因演算法來找出各個不同影像對之間最佳的轉換參數。將以基因演算法尋找到的最佳轉換參數記錄下來,形成資料庫,以查表方式減少所需之大量運算時間。第二階段利用爬山法快速收斂的性質,對於這些參數進行微調,以符合新的影像轉換所需。本論文之演算法整合兩者之優點,以求得更快更準確的結果。
實驗選取75對臨床X光片。50對用於建立參數資料庫。另外25對用於測試系統的轉換結果。並以虛擬色彩重疊回原影像呈現差異處。實驗結果與基因演算法及免疫演算法選點校正比較。在時間耗損上,提出之方式可於61秒±8秒內得到結果,而基因演算法則為1,580秒±124秒,免疫演算法為695秒±168秒。校正品質上,本方式之PSNR值為33.08±3.59dB,基因演算法為32.88±3.37dB,免疫演算法為32.84±3.72dB。觀察者在像差影像下觀察之一致性可達84%,對於系統的平均滿意度則為84.7%。本論文提出以查表方式減少校正牙科放射性影像之時間及電腦之運算複雜度,一分鐘即顯示之牙科像差放射線影像於實際臨床有一大突破。

Dental subtraction radiography is accomplished by using a special frame to standardize radiographic geometry. However, storage and sterilization of film holders are exhausting and dentists don’t use in daily practice. This study accomplishes a framework to hasten registration of dental subtraction radiography and subsequent to be used in clinical dental subtraction radiography. The proposed method has two phases and combines two kinds of evolutionary computing to speed up computation. Genetic algorithm (GA) is applied to find the optimal parameters of transform function between two radiographic image pairs. The optimal parameters found by GA are recorded and stored in the database. With look-up table strategy, the database provides excellent initial parameters for hillclimbing algorithm adjusting to optimal solutions in short time.
We choose 75 dental radiographic image pairs for this study. 50 image pairs are randomly assigned to establish the database. The other 25 image pairs are used in testing the performance of proposed system. Subtraction image is psedocoloring superimposed to original image for enhancing inspection of radiographs. The performance of proposed method is compared to GA method and automatic point correspondence method based on artificial immune system algorithm (AIS method). Time requirements for image registration of proposed method, GA method, and AIS method are 61.30±8.4 seconds, 1,580.48±124.22 seconds, and 695.03±168.20 seconds, respectively. Quantitative analysis for registration result is evaluated by calculating the peak signal to noise ratio (PSNR). The PSNRs of proposed method, GA method, and AIS method are 33.08 3.59dB, 32.88 3.37dB, and 32.84 3.72dB, respectively. Interobserver agreement is about 84% agreement to the same alterations on all radiographs under system assisting. Satisfaction questionnaire for clinical assistance is overall about 84.37% satisfactions.

中文摘要....................................................I
Abstract..................................................II
Acknowledgment............................................IV
Table of Contents..........................................V
List of Table............................................VII
List of Figures.........................................VIII
Chapter 1 Introduction....................................1
1.1 Dental Subtraction Radiograph.................3
1.2 Motivation....................................5
1.3 Organization of the Thesis....................5
Chapter 2 Background and Related Work.....................6
2.1 Preliminary Image Registration................6
2.1.1 Definition..................................6
2.1.2 Contribution................................7
2.1.3 Limitation..................................8
2.2 Review of Image Registration Method...........8
2.2.1 Feature based method........................9
2.2.2 Intensity based method.....................10
2.3 Related Techniques...........................10
2.3.1 Registration transformation................11
2.3.2 Optimal algorithm..........................12
2.3.2.1 Genetic algorithm........................12
2.3.2.2 Hillclimbing algorithm...................15
2.4 Related Research for Comparison..............15
2.4.1 Feature based method: A case study on AIS
method....................................16
2.4.2 Intensity based method: Case studies on
ROI-based down hill simplex method and
GA-based automatic retiming method........17
Chapter 3 Proposed Framework.............................20
3.1 Flowchart of Proposed Framework..............20
3.2 Method of Proposed Framework.................21
3.2.1 Training phase.............................21
3.2.1.1 Transformed function.....................23
3.2.1.2 Genetic algorithm........................23
3.2.1.3 Evaluation and database formation........26
3.2.1.4 Idea of lookup table.....................27
3.2.2 Testing phase..............................28
3.3 Performance Evaluation.......................30
3.3.1 Data.......................................30
3.3.2 Evaluation indices.........................32
3.3.3 Image quality..............................34
Chapter 4 Results........................................36
Chapter 5 Conclusions and Future Work....................40
5.1 Conclusions..................................40
5.2 Future Work..................................41
Reference ................................................42
Appendix .................................................48

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