超音波影像含有假影和斑紋，使特徵和組織邊界難以觀察。在此碩論中，我們提出基於多窗口局部變異數特徵偵測之超音波影像銳化與斑紋去除技術，來主動強化超音波影像的邊界和特徵以及消除斑紋雜訊。影像銳化是由加強影像中特徵的高頻訊息，使其影像特徵看似更加銳利。然而，超音波影像中的斑紋也是高頻訊息，往往會被誤認為特徵而被錯誤的加強。因此，我們提出一個能在斑紋雜訊下偵測超音波特徵的方法來進行影像銳化 – 多窗口局部變異數特徵偵測法。此方法利用超音波斑紋的統計特性 – 均勻非特徵的斑紋雜訊區域其機率密度函數的變異數是固定的，藉由局部變異數的值來偵測特徵。此方法在計算局部變異數時，使用多個不同大小的窗口來偵測，再將所有偵測的特徵融合為一個壓抑斑點假特徵的特徵影像，使得接下來的影像銳化較文獻中常見超音波影像銳化技術能更有效地銳化超音波影像中實際的特徵及邊界。此外，基於所提出的多窗口局部變異數特徵偵測，我們也提出一個適應性非局部均值濾波的方法來消除超音波影像中的斑紋雜訊。基於偵測所得的特徵適應性地調整影像中各個位置的濾波強度。特徵處就給予較低的濾波強度，使其在消除斑紋雜訊的同時亦保留超音波影像中重要的特徵。結果顯示所提出適應性非局部均值濾波的方法較目前文獻中用於斑紋雜訊消除的非局部均值濾波技術可以更平衡地取得去斑紋雜訊效果。

Ultrasound images suffer from speckle noises and several artifacts that degrade image contrast and cause difficulties in observing borders and features. In this thesis, we propose a novel image sharpening algorithm and a new speckle reduction technique using multi-window local variance detected features to actively enhances edges and borders and suppress speckles in ultrasound images. Image sharpening generally is done by amplifying the high-frequency components representing features in the image. However, ultrasound images suffer speckle noises which are also with high frequencies and thus tends to be undesirably sharpened. In order not to sharpen speckle noises, a feature mask is needed to adaptively adjust the enhancing strength. Here, we present a multiscale local variance (MLV) feature detection technique featuring robust feature detection under speckle noises for ultrasound images. Based on the constant variance characteristic of Fisher-Tippet distribution in log-compressed ultrasound images, MLV use local variances to detect features. MLV calculates the local variance using a multi-window framework, in which several window sizes are used to detect features of different scales, then being combined together to obtain one robust and fine feature map. Such a robust and fine feature map enables more effective image sharpening than the techniques commonly used for ultrasound image enhancement in the literature. The efficacy of the proposed MLV based image sharpening algorithm is tested on simulation and real ultrasound B-mode images, showing improvements in objective metrics and subjective evaluations. In addition, based on MLV feature detection, we propose an adaptive non-local means (ANLM) filter for ultrasound image speckle reduction. ANLM uses spatial-varying smoothing parameters that are determined by the MLV detected features. Feature regions are given weaker smoothing strength for feature preservation. Results show that ANLM generates balanced and optimal speckle reduction results than the conventional non-local means filtering.

Contents
摘要 ii
Abstract iii
Introduction 1
Related Work 8
Methods 13
Results 21
Discussion 38
Conclusions 43
References 44

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