臺灣博碩士論文加值系統

衛生福利部在2023年統計，全國患有牙根尖相關疾病患者高達8成，其中有2成處於嚴重狀況，且牙根尖炎是相當常見的，通常患者需要長期觀察與治療一次療程，初期由於口腔清潔不完善導致發炎，症狀至中後期，侵蝕牙齒表面後沿著神經根管到牙根尖處，且患者會隨著年齡增長逐漸惡化，牙科醫師以X-ray影像檢查患者症狀程度，判斷如何進行進一步療程，然而單張不同時間點、多個時間點影像無法達到相同標準的灰階對比度，加上醫師或放射師判讀不同，造成醫療爭議，本論文開發新型影像強化演算法，將多時間點不一致的X-ray強化影像達到相同灰階標準的效果。
本研究新型影像強化演算法在Windows作業系統已先行測試開發，開發軟體為MATLAB，將X-ray影像灰階範圍進行處理，背景等雜訊像素去除與歸一化(be normalized)，多次實驗中得到常態分布(Normal distribution)中距離平均值的2個標準差之內有最佳數值分布，最後進行核心灰階相近最小值合併，得到強化影像。本論文共使用204筆單一時間點、4筆多時間點X-ray影像為樣本，新型影像強化演算法正確率最高達到91.07%，最低83.93%，原始影像最高為82.14%，最低69.64%，透過穩定影像灰階度、對比度達到提升正確率，同時輔助醫師降低時間點與診斷上不一致的可能性。
研發完新型影像強化演算法後，本研究利用macOS與iOS的程式語法相通等便利性，於iOS平台上使用Xcode以Swift程式語言整合演算法並通過多種優化方法與軟體套件管理系統，透過開發環境設置、安裝過程大幅度提升效能、性能，設計一款iOS APP以結合新型影像強化演算法輔助醫師臨床看診時增加便利性，成為本論文最終目標。

The Ministry of Health and Welfare reported in 2023 that 80% of the population suffers from apical periodontitis, with 20% in severe condition. Apical periodontitis is quite common, and patients usually require long-term observation and treatment. In the early stages, inflammation occurs due to inadequate oral hygiene. As the condition progresses, it erodes the surface of the teeth and extends along the nerve root canal to the apex of the tooth. The condition tends to worsen as patients age. Dentists use X-ray images to assess the severity of the symptoms and determine further treatment. However, single images taken at different times and multiple images taken at different times do not achieve the same standard of grayscale contrast. Additionally, different interpretations by physicians or radiographers lead to medical disputes. This paper proposes a new image enhancement algorithm to achieve consistent grayscale standards for multiple inconsistent X-ray images.
The new image enhancement algorithm developed in this study has been tested and developed on the Windows operating system using MATLAB. It processes the grayscale range of X-ray images, removes background noise and normalizes the pixels, and in multiple experiments, it was found that the best value distribution lies within 2 standard deviations from the mean of the normal distribution. Finally, it performs core grayscale merging to obtain the enhanced image. This paper uses 204 single-time-point and 4 multi-time-point X-ray images as samples. The highest accuracy of the new image enhancement algorithm reaches 91.07%, with the lowest being 83.93%. In comparison, the original images have a maximum accuracy of 82.14% and a minimum of 69.64%. Through stabilizing image grayscale and contrast, the algorithm achieves improved accuracy, while also assisting physicians in reducing the possibility of inconsistency in timing and diagnosis.
After developing the new image enhancement algorithm, this study leverages the convenience of macOS and iOS programming language compatibility. It integrates the algorithm on the iOS platform using the Swift programming language and passes through various optimization methods and software package management systems. By setting up the development environment and installation process, it greatly enhances performance and efficiency, designing an iOS app to combine the new image enhancement algorithm to assist physicians in clinical diagnosis, ultimately achieving the goal of this paper.

考試委員審定書 I
致謝 II
摘要 III
Abstract IV
目錄 VI
圖目錄 VIII
表目錄 IX
第一章 緒論 1
1.1 研究背景與目的 1
1.2 文獻回顧 3
1.2.1 影像強化 3
1.2.2 多時間點影像強化 4
1.2.3平台選擇 5
1.2.4 醫療應用行動APP 6
1.2.5 iOS APP開發環境 7
1.3 論文架構 10
第二章 理論背景 11
2.1 基礎影像強化方法 11
2.1.1 Histogram Equalization(HE) 11
2.1.2 Adaptive Histogram Equalization (AHE) 12
2.1.3 Contrast Limited Adaptive Histogram Equalization(CLAHE) 13
2.1.4 多時間點影像強化對比度 14
2.2 iOS軟體開發 15
2.2.1 iOS作業系統 15
2.2.2 iOS APP開發環境 16
2.2.3 iOS APP程式語言 18
第三章 研究方法 20
3.1 研究範圍與對象 20
3.2 iOS APP 21
3.2.1 iOS APP整合開發 21
3.2.2 Icon設計&啟動畫面 23
3.2.3 UI介面設計 26
3.2.4 原始影像輸入 - CocoaPods 30
3.2.5 新型影像強化演算法 31
3.2.6 強化影像輸出 - CGImage 36
3.2.7 介面UI顯示 37
3.3 iOS APP程式優化 38
3.3.1 編譯執行緒 38
3.3.2 建構主動架構(Build Active Architecture) 39
3.3.3 更新與調整構建系統 40
3.3.4 構建設置-使用並行構建 41
3.3.5 構建設置-調試數據格式 42
3.3.6 調整編譯優化級別 43
第四章 結果與討論 44
4.1 iOS APP效能評估 44
4.2 iOS APP新型強化影像 45
4.2.1 iOS APP影像輸出 45
4.2.2 Windows版與iOS版驗證 47
4.3 使用者實測 49
4.3.1 醫師準確度分析 49
第五章 結論與未來展望 51
參考文獻 52

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