近年來進行牙齒矯正治療的人越來越多，矯正治療的時間往往依每人的牙齒情況而有所不同。因此牙醫師們設計了一組參數，用於評估治療時間，以及不正常牙齒治療的可行性。本論文提出一套方法來建立穩固的量測系統，以協助牙醫師獲得可靠的牙齒參數。環口攝影藉由環繞口腔一周所拍攝的影像，將立體的口腔區域以二維影像作呈現，可以用來觀察整個口腔區域的牙齒及骨頭情形，所以矯正牙科經常採用此影像進行參數量測與評估。
由於環口影像為全方面的取像，對於細節的描述並不清楚，所以對於每顆牙齒做細部影像分割會有一定難度。針對影像資訊的不完整，本論文提出使用以模型為基礎的方法進行影像分割。本論文所提出的影像處理系統依序可分成四大部分：影像前處理，模型訓練，牙齒輪廓分割以及參數量測。影像前處理針對影像大小及亮度分布做一致化設定，再對感興趣區域做影像強化；模型訓練部分則利用ASM (Active Shape Model) 訓練牙齒模型，並針對形變依據的能量方程式及其權重值提出一套完整的訓練方法；然後依據訓練階段所得到的模型，進行牙齒輪廓的形變，以分割牙齒區域進而將輪廓貼合在影像的牙齒上；再獲得牙齒輪廓後，可再利用牙齒輪廓進行自動化的參數量測。
實驗結果分成定性與定量分析，我們將比較自動化分割的牙齒輪廓與專家所點選的標準值差異性，以及專家所量測的參數結果值與自動化的參數量測值的差異。由實驗結果得知，自動化牙齒輪廓分割結果與專家所點選的標準值相似度極高，且自動化量測的參數值與專家所量測的參數值差異很小。因此，本論文所提出的方法能夠提供自動且方便的工具，以幫助牙醫師更準確且一致地進行牙齒矯正之評估與治療。

In recent years, there are more people taking the orthodontic treatment. The treatment process and consuming time are usually different for patients with dissimilar symptoms. For better treatment outcomes, dentists design a set of parameters for orthodontic evaluation. In this thesis, an automated system is proposed to assist dentists in obtaining reliable measurement of these parameters. Dental panoramic radiograph has been widely used in the clinical diagnosis of stomatology. It is used to observe both the teeth and bones in oral cavity. Because the panoramic radiograph provides a wide range of oral view, the detailed information of a single tooth is not always available so that the segmentation of a tooth contour is not so simple.
The proposed system can be divided into four stages: image preprocessing, model training, tooth contour segmentation and assessment of parameters. In the image preprocessing, the image size is first made the same based on the region of oral cavity, the histograms are then normalized, and the regions of interest (ROIs) are locally enhanced at last. There are three terms in the model training stage: the mean shape and mean image model training, the energy function training and the mean weights training. The method to automatically segment the tooth contours by using the statistical model is explained in the stage of tooth segmentation. Finally, the automatic assessment of orthodontic parameters is explained at the last stage.
The experimental results comprise the qualitative and quantitative analyses. The experimental results of the proposed method have been compared to the ground truth from experts for both the tooth contours and the orthodontic parameters. It has been shown that the proposed method can obtain similar accuracy and consistent measurement in helping dentists for treatment evaluation.

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