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研究生:陳冠志
研究生(外文):Guan-Zhi Chen
論文名稱:降低加乘性雜訊的混雜濾波方法
論文名稱(外文):A Heterogeneous Filtering Method for Multiplicative Noise
指導教授:翁世光
指導教授(外文):Shyh-Kuang Ueng
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:45
中文關鍵詞:超音波醫學影像加乘性雜訊混雜濾波
外文關鍵詞:Ultrasound Medical ImageMultiplicative NoiseHeterogeneous Filtering Method
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超音波影像容易產生亮斑雜訊,導致組織輪廓變得模糊,影響影像的品質,因此抑制亮斑雜訊在超音波影像的視覺化中是非常重要的。線性濾波器可以抑制亮斑雜訊,但是也會造成影像的模糊化。使用中間值濾波器可以獲得較好的結果,然而中間值濾波器會在灰階值不均勻的區域產生人為圖樣,影響觀察者的判斷。我們會在本論文中提出一種嶄新的超音波去雜訊演算法。在本方法中,我們會在每一個像素算出灰階值的擴散矩陣。然後利用擴散矩陣的特徵值及特徵向量進行結構分類。最後利用結構的種類選擇合適的濾波器在該像素進行雜訊抑制及特徵加強。測試結果顯示出本方法能夠有效改善超音波影像的品質,我們的方法對亮斑雜訊和一般加乘性雜訊有良好的抑制效果,並且能夠加強組織特徵。
Ultrasound images are prone to speckle noise. Speckles blur key features which are essential for diagnosis and assessment. Thus despeckling is important in ultrasound data visualization. Linear filters suppress speckles, but they also smooth out key features. Median filter based despeckling algorithms can produce better results. However, they may produce artifact patterns in the resulted images and over-smooth non-uniform regions. In this thesis, we presents an innovative despeckle algorithm for ultrasound images. The diffusion tensor of intensity is computed firstly at each pixel in our proposed method. Then the eigen system of the diffusion tensor is calculated and employed to detect and classify the underlying structure. After finishing above mentioned steps, the computed structure type is utilized to select a feasible filter to suppress speckles and enhance the detecting feature. The test results show that the proposed despeckle method reduce speckles and enhance tissue boundaries for ultrasound data. In another test, the proposed despeckle method is employed to remove multiplicative noise for grey level images. The experimental results show that our method is also effective for reducing multiplicative noise and enhancing features for grey level images.
摘要 I
Abstract II
1. 導論 1
2. 相關研究 5
3. 背景知識 9
3.1 海森擴散矩陣 9
3.2 結構分類原則 10
3.3 超音波影像的海森矩陣計算方式 12
3.4 結構顆粒度(Granularity)的偵測 13
4. 結構型態分類方法 16
4.1 結構類型與特徵值的關係 16
4.2 型態分類方法 17
4.3 改善結構分類 20
5. 基於結構型態的混合濾波器 23
5.1 去雜訊濾波器選擇決策 23
5.2 疊代濾波流程 25
6. 實驗結果 27
6.1 超音波雜訊影像處理 27
6.2 加乘性雜訊影像處理結果 31
7. 結論 42
參考文獻: 43

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