超音波斑點雜訊在影像上會降低成像的對比，並且會使影像上的病理特徵被其掩蓋住;因此導致臨床診斷上的不準確性，而此特性亦會造成醫學計算機視覺技術的受限，例如病灶區域的自動檢測和分割。儘管多年來已經提出了許多斑點雜訊減少方法，例如優化貝氏非區域均值濾波器和非等向擴散斑點雜訊抑制濾波器等，但它們仍然存在兩個主要問題:在影像特徵細節上的保留不足，包括結構邊緣，紋理和點狀結構等，及過度模糊化使影像看起來像是人為。為解決以上這兩個問題，我們提出了一種新的中央窩化非區域均值濾波技術，其靈感來自於人類的視覺系統。傳統的非區域均值濾波器透過在圖像內的不同區域搜索類似的區塊來進行去除斑點，透過由目標區塊和搜索窗口內區塊之間的歐氏距離評估兩者的相似度來計算權重。在我們的技術中，使用了中央窩化自相似性而不是傳統的自相似性。這種自相似性產生模擬人類視網膜特性的新區塊運算子，銳化區塊中心的像素並使其周邊模糊。此外，在許多文獻中，搜索窗口大小和目標區塊大小此兩參數設置均不一致;然而，在我們提出的研究中，它們兩者均可從超音波成像的角度上進行參數設置，即根據解析度單元的大小進行調整，進而可應用在不同規格的超音波成像系統上。我們提出的超音波斑點雜訊抑制技術之性能也與目前最先進的方法進行了比較。實驗結果表明，我們提出的方法可以更有效地保留結構邊緣細節，紋理和點狀結構，同時有效地去除斑點。並且客觀與系統地比較對比度雜訊比和對比度量測的定量測量。

Ultrasound speckle-noise debases imaging contrast and hides anatomical details; thus causing inaccuracy in clinical diagnosis as well as limiting the applications of medical computer vision techniques such as automatic detection and segmentation of lesion regions. Although many speckle reduction methods such as optimized Bayesian nonlocal means (OBNLM) and speckle reducing anisotropic diffusion (SRAD) filters have been proposed for years, they still suffer two significant problems - insufficient preservation of specific details including structural edge, textures and point-like structures as well as inordinate blurring making image appearance artificial. To solve these two problems, we propose a novel foveated nonlocal means despeckle filtering technique, inspired by the human visual system. Conventional NLM filters despeckles via searching for analogous patches at different areas within the image and then estimating the weighting by the degrees of similarity appraised by a windowed Euclidean distance between the target and searching patches. In our technique, foveated self-similarity is used instead of conventional self-similarity. The foveated self-similarity is based on a new patch operator mimicking human retina properties, sharpening patch pixels in the center and blurring them near the periphery. Moreover, throughout the literature, the tuning of the search window and patch sizes and other parameters is not consistent; nonetheless, in this study, they are tuned universally from ultrasound imaging perspective, i.e., according to the size of resolution cell, which allows the adaption to different imaging systems and settings. The performance of our proposed technique in speckle reduction is also compared with those of the state-of-the-art approaches. Experimental results indicate that the proposed method can more effectively retain structural edge details, textures, and point-like structures while removing speckles forcefully. Quantitative measures such as contrast-to-noise ratio and contrast measure are also presented.

摘要 I
Abstract II
Table of Contents IV
List of Figure VII
List of Table XVI
Chapter 1 Introduction 1
1.1 Ultrasound speckle noise 1
1.1.1 Cause and Influence of Speckle Noise 1
1.1.2 Model of Speckle Noise 4
1.2 Speckle Reduction Technologies of Ultrasound 6
1.2.1 Local Means Filter 6
1.2.2 Non-local Means Filter 7
1.2.3 Optimized Bayesian Non-Local Means Filter 14
1.2.4 Perona-Malik Diffusion Model 19
1.2.5 Speckle Reducing Anisotropic Diffusion 22
1.3 Motivation and Purpose 24
1.4 Organization of Thesis 27
Chapter 2 Materials and Methods 29
2.1 Foveated Self-Similarity Nonlocal Means 29
2.1.1 Foveation 29
2.1.2 Foveation Operator 31
2.1.2.1 Foveated Distance and Foveated Patch 31
2.1.2.2 Constrained Design for the Foveation Operator 33
2.1.2.3 Design Foveation Operator 35
2.1.2.4 Foveated NLM 40
2.2 Simulation 41
2.3 Adjust Parameter Based on PSF 44
2.4 Image Quality indices 48
2.4.1 Contrast-to-noise Ratio (CNR) 48
2.4.2 Contrast Measure (CM) 49
2.4.3 Edge Preservation Index (EPI) 50
2.5 Clinical Data 51
Chapter 3 Experimental Results and Discussion 55
3.1 Optimizing Parameters of Fov-NLM 55
3.2 Experiments for Simulated Ultrasound Images 66
3.3 Experiments for Clinical Ultrasound Images 75
Chapter 4 Conclusions and Future Works 85
4.1 Conclusions 85
4.2 Future Work 86
References 88

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