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研究生:盧建勳
研究生(外文):Lu, Chien-Hsun
論文名稱:電腦模擬顏面整形手術術後臉型之準確性及在不修改模擬軟體情況下增加模擬準確度
論文名稱(外文):Evaluation and improvement of computer simulation of the results in plastic surgery
指導教授:劉立劉立引用關係
指導教授(外文):Liu, Li
學位類別:碩士
校院名稱:臺北醫學大學
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:2003
畢業學年度:91
語文別:中文
論文頁數:55
中文關鍵詞:視訊影像術後模擬類神經網路
外文關鍵詞:video imagingsimulationartificial neural network
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隨著資訊科技的進步,視訊影像的模擬逐漸被應用在顏面整形手術的術後
模擬預估上。這些模擬影像會大大影響患者決定是否接受此一手術而且也
可提供醫師訂定治療計畫的資訊;因此,這些模擬是否真實可信是極需要
探討的。本論文的目的在評估此類影像模擬的可信度及真實性並提出一種
改善模擬準確度的方法。所使用研究對象為三十位因上下顎前凸而接受雙
顎正顎手術的成人患者,將這些患者的術後模擬影像和實際手術後的影像
作一比較,而類神經網路則用來改進模擬的準確度。其結果發現鼻尖點的
模擬誤差最小,平均誤差小於1 mm;模擬較可信的點在鼻尖及軟組織A 點。平均每次模擬出現誤差小於2 mm 的機會約為50%;在應用類神經網路改善後,模擬誤差大幅減少,X 軸平均誤差改善率為43.9%,Y 軸平均誤差改善率為-6.6%;平均每次模擬出現誤差小於2 mm 的機會約為84.5%。整體來說,利用電腦影像來模擬顏面正顎整形手術出來的模擬是值得作為參考的,然而其準確度還需要加以改進才能作為臨床訂定醫療計畫的依據,而類神經網路則提供了一個不錯的改良方法。

As the advancement of computer technology, video image simulation became more and more frequently used in the simulation of craniofacial surgery. The simulation will greatly affect the decision making of the patients and also provide information to the surgeon and orthodontics. Thus, how real would the simulation present was primary concern. The purpose of this thesis was to evaluate the accuracy and reliability of the post-surgical simulation and try to find out a method to improve the simulation. Thirty bimaxillary protrusion patients who under went two jaw surgery were taken into consideration. The simulation was compared with the real post-surgical facial profile. Artificial neural network was used to make the simulation more accurate. The results showed the most accurate area located at tip of nose with the average errors were less than 1 mm. The more reliable area located at tip of nose, and soft tissue A point. The average probability of every simulation that the errors were smaller than 2 mm was about 50%. After applying the artificial neural network to the input data, the simulation errors were reduced.The improvement rate of average simulation errors in X-axis and Y-axis were 43.9% and -6.6%. The average probability of every simulation that the errors were smaller than 2 mm was 84.5%. In general, the present computer simulation of the craniofacial plastic surgery is good for reference but efforts still need to be done before it can provide enough information for surgical treatment plan. Artificial neural network may provide a good way to achieve this goal.

目錄
標題....................................................................................................................... i
審定書.................................................................................................................. ii
上網授權書.........................................................................................................iii
國科會授權書..................................................................................................... iv
誌謝..................................................................................................................... vi
目錄.................................................................................................................... vii
表目錄.................................................................................................................. x
圖目錄..................................................................................................................xi
中文摘要.............................................................................................................xii
英文摘要............................................................................................................xiii
第一章緒論.........................................................................................................1
第二章文獻查證.................................................................................................4
2.1 電腦視訊影像模擬的影響.....................................................................4
2.2 影像模擬軟體預估手術後臉型外觀的準確性之文獻探討.................5
2.3 類神經網路在醫學上應用之文獻探討.................................................6
第三章研究材料與方法.....................................................................................9
3.1 研究動機.................................................................................................9
viii
3.2 研究目的.................................................................................................9
3.3 研究方法.................................................................................................9
3.3.1 模擬軟體模擬準確性評估...........................................................9
3.3.1.1 模擬軟體介紹......................................................................9
3.3.1.2 研究對象............................................................................13
3.3.1.3 座標軸定義及紀錄量取....................................................15
3.3.1.4 採用方法正確性評估........................................................17
3.3.2 利用類神經網路增加模擬準確性的方法.................................17
3.3.2.1 類神經網路的Training set 及Target set 之獲得.................17
3.3.2.1.1 Training set 之獲得..........................................................17
3.3.2.1.2 Target set 之獲得..............................................................18
3.3.2.2 類神經網路設計........................................................................20
3.3.2.3 類神經網路對模擬改善效果評估............................................23
第四章分析與結果...........................................................................................24
4.1 採用方法正確性評估...........................................................................24
4.2 模擬準確性...........................................................................................24
4.2.1 水平面.........................................................................................24
4.2.2 垂直面.........................................................................................26
ix
4.2.3 模擬誤差分布圖.........................................................................27
4.2.4 影像模擬例圖.............................................................................32
4.3 模擬改進...........................................................................................33
4.3.1 改善後水平面.............................................................................33
4.3.2 改良後垂直面.............................................................................34
4.3.3 改善後模擬誤差分布圖.............................................................36
4.3.4 改善後影像模擬例圖.................................................................40
第五章討論.......................................................................................................42
5.1 模擬準確度探討...................................................................................42
5.2 類神經網路改善探討...........................................................................43
5.2.1 改善效果.....................................................................................43
5.2.2 改善效率.....................................................................................46
5.2.3 改善後誤差分布.........................................................................50
第六章結論與建議...........................................................................................52
參考資料.............................................................................................................53

53
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