本文選題：圖像分割 + 超聲圖像��； 參考：《西南石油大學》2017年碩士論文

【摘要】：醫(yī)學成像作為現(xiàn)在醫(yī)學的重要組成部分在醫(yī)學理論和臨床診斷上都有重要的意義。超聲圖像由于其無損、廉價、實時運行、安全的特點在眾多醫(yī)學圖像中有著重要的地位。與其他醫(yī)學成像技術相比,超聲在臨床診斷上的應用有著顯著的普及度,所以對超聲圖像的處理技術的研究有著重要的意義。圖像分割作為圖像分析和信息醫(yī)學的關鍵,一直都受到研究者們的重視。然而由于超聲圖像在成像精度上很難和CT、MRI技術抗衡,這造成了超聲圖像分割的一大難點。超聲圖像特有的散斑紋理增加了超聲分割的難度。在超聲圖像處理領域有著大量的文獻研究如何去除散斑。此外,超聲散斑也被指出和組織性質(zhì)有著一定的的關系。如何應用散斑為組織分割提供信息的同時減小散斑對超聲圖像的影響,是超聲分割研究的重點。除此之外,超聲散斑紋理在不同的超聲圖像中有著較大的差異,除開某些統(tǒng)計模型,并沒有一個較好的固定的紋理模型對其進行描述的隨機性也是超聲圖像分割的難點之一。本文針對超聲散斑紋理的隨機性和二義性提出了兩種基于非局部思想的算法。二義性是指,散斑影響了超聲的成像質(zhì)量的同時于組織分布有密切的聯(lián)系。第一,本文針對超聲散斑紋理隨機性提出了非局部塊聚類特征提取算法,解決了超聲散斑紋理隨機性較強時,難以提取統(tǒng)一特征值的問題。第二,本文針對超聲散斑紋理的二義性提出了自適應的特征提取算法,解決了一般去噪預處理時容易將散斑攜帶的組織特征信息一并去掉的問題。并通過對比實驗證明了這兩種方法的有效性。最后,本文綜合應用這兩種方法,提出了基于三維塊匹配的超聲紋理提取方法,并對超聲圖像進行了分割。此外,將該方法做出了改進,解決了該方法在超聲弱回聲處分割不穩(wěn)定的問題。并通過對比實驗對這兩種方法進行了有效性分析。
[Abstract]:As an important part of current medicine, medical imaging plays an important role in medical theory and clinical diagnosis.Ultrasonic images play an important role in many medical images because of their nondestructive, inexpensive, real-time operation and safety.Compared with other medical imaging techniques, ultrasound is widely used in clinical diagnosis, so the research of ultrasonic image processing technology is of great significance.As the key of image analysis and information medicine, image segmentation has been paid attention by researchers all the time.However, the imaging accuracy of ultrasonic image is very difficult to compete with CT MRI technology, which leads to a big difficulty in ultrasonic image segmentation.The special speckle texture of ultrasonic image increases the difficulty of ultrasonic segmentation.In the field of ultrasonic image processing, there is a lot of literature on how to remove speckle.In addition, ultrasonic speckles were also noted to have a certain relationship with tissue properties.How to use speckle to provide information for tissue segmentation while reducing the influence of speckle on ultrasonic image is the focus of ultrasonic segmentation.In addition, ultrasonic speckle texture has great differences in different ultrasound images. Apart from some statistical models, it is also one of the difficulties in ultrasonic image segmentation that there is not a good randomness of the description of ultrasonic speckle texture model.In this paper, two algorithms based on nonlocal idea are proposed for randomness and ambiguity of ultrasonic speckle texture.Ambiguity means that speckle affects the imaging quality of ultrasound and is closely related to tissue distribution.Firstly, a nonlocal block clustering feature extraction algorithm is proposed for the randomness of ultrasonic speckle texture, which solves the problem that it is difficult to extract uniform eigenvalues when ultrasonic speckle texture is stochastic.Secondly, an adaptive feature extraction algorithm is proposed for the ambiguity of ultrasonic speckle texture, which solves the problem that the information of tissue features carried by speckle can be removed simultaneously during the general denoising preprocessing.The effectiveness of the two methods is proved by comparative experiments.Finally, this paper proposes an ultrasonic texture extraction method based on 3D block matching, and segments the ultrasonic image by using these two methods.In addition, the method is improved to solve the problem of unstable segmentation at the weak echo of ultrasound.The effectiveness of the two methods is analyzed through comparative experiments.
【學位授予單位】：西南石油大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R445.1;TP391.41

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