摘 要
數位影像處理是一種將圖像進行分析、加工和處理使其滿足人類視覺、心理或是其他需要的一門技術。在影像形成的理論基礎中，光學理論佔有極重要的地位，影像處理也可以說基於光學理論處理的基礎下，為信號在圖像領域上的一種應用技術。
影像強化、影像分割、影像特徵之萃取與表示乃是影像分析之基本工作，完成分析後之各項結果可以提供後續特定要求處理之參考，如影像辨識等。影像強化技術在影像品質的維繫上，佔有極為重要的地位。依據不同的處理目的，影像強化技術的難易度亦有所不同，例如要顯示較暗影像的資訊，我們必須先提高影像的強度才可以看見資訊或是影像的細節。如此才能為下一個步驟提供良好的素材，以提升品質或是系統的效率。
影像分割的主要工作是縮小影像的範圍，僅留下影像中重要的部份，或是因某種特定需求必須留下的部份。因此，影像分割技術在各個影像處理應用領域中是相當重要的一個環節。
本論文提出將類神經網路技術應用在影像分割上，該技術除醫學影像適用之外，對自然影像也有相同的效果。此外為提高影像的分割效率亦將影像強化技術加入其中以提高影像分割技術的效率。依據我們的實驗證明，我們提出一個可以成功結合影像強化與影像分割的架構，並且可以達到提高影像分割的效率。

Abstract
Digital image processing is a type of technology that may be used in image analysis, the processing of which causes it to satisfy the human vision and psychological or other particular purposes. The theoretical basis of the image formation in optical theory plays an important role in image processing, and optical theory is the basis for the signal processing of the image field in an application technology.
The basic works of image analysis are as follows: image enhancement, image segmentation, image feature extraction, and representation of the image. Therefore, the complete analysis of the results can provide a reference for subsequently processing specific requirements, such as image recognition. Image enhancement technology is used to maintain the image quality, and plays a vital role. Image enhancement techniques vary depending on the purpose of processing, for example, to display detailed information of the darker image, the intensity of the image must be improved before proceeding to the next step for provision of optimal material to improve the quality or efficiency of the system.
The main work of image segmentation is to narrow the scope of an image to retain only a crucial part of the image or a particular part that must be retained. Therefore, image segmentation techniques are a crucial aspect in various image processing applications.
This Dissertation presents a neural network technology used in image segmentation, and the application of this technology in medical imaging, in which the images of nature have the same effect. In addition, to improve the efficiency of image segmentation, image enhancement technology is usually included to improve the influence of image segmentation. Our experiment results demonstrate a successful combination of image enhancement and image segmentation framework to improve the efficiency of image segmentation.

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