醫學影像辨識是一件頗為複雜的工作，必須結合影像處理、電腦圖學及解剖學等眾多相關領域的知識才能完成。在本論文中我們建立了一個器官三維重建流程：經由影像讀取、影像辨識、辨識結果評估、加上辨識結果修正，最後將修正結果重建為器官三維模型，以提供醫學或是教學上之輔助。
　　在影像辨識方面，建立在我們已發展的一套系統─「以黑板架構為基礎之互動式影像辨識系統」之上。我們加入了數個新的辨識知識源：骨盆腔骨骼、腹腔胰臟、以及腫瘤等，以增加辦識器官的種類數。
　　影像辨識的結果，我們採用描述物體外圍的輪廓線來表示。因此，在辨識結果評估方面，我們發展了一套「輪廓線評估系統」。此輪廓線評估系統是利用辨識出來的器官輪廓線及其原始影像，做一成果優異性評估，並且將評估結果提供給其它系統，以利其它系統對器官輪廓線再做修正，而得到最佳辨識之結果。本論文中亦說明此評估系統與其它醫學影像系統的整合應用。
　　而辦識、修正後的器官輪廓線，我們採用了另一套系統 ─ 「以輪廓線為主的三維物件重建系統」，用這些器官輪廓線重建出該器官的三維模型，以提供醫學上的種種應用。

　　The recognition of a medical image is a complex task. We need to integrate the knowledge of image processing, computer vision and anatomy to complete the task. In this thesis, we build up a process for reconstruction of 3D organ. It starts from reading images, image recognition, evaluation, and finally reconstructs the 3D organ. It will help the user to understand more about the organ in the applications of the medicine or education.
　　In the part of image recognition, we develop five knowledge sources based on the recognition system of the MiracViewer. There are three knowledge sources for recognition of pelvic bone, one for recognition of abdominal pancreas, and one for recognition of the tumor.
　　We use the contour that used to describe the outer edge of an object to represent the results of the recognition. Therefore we develop a “Contour Evaluation System” for evaluation of these recognition results. The evaluation system uses the resultant contours and original image to do a serial evaluation. It will provide the quality scores of the contour, and some adjust information that can provide for other system to correct the contour. We also illustrate the integration with other medical image systems.
　　We use another system named 3DBuilder to reconstruct the 3D organ with the recognized or modified contours. They will provide lots of applications in medicine.

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