發(fā)布時(shí)間：2018-10-18 13:58

【摘要】：低場(chǎng)或快速成像得到的磁共振圖像信噪比往往較低,臨床應(yīng)用中常常采用增加采集次數(shù),將多幀圖像累加平均來(lái)提高圖像的信噪比。但是,在多次采集過(guò)程中,人體的自主或不自主的運(yùn)動(dòng)使得多幅圖像之間產(chǎn)生相對(duì)偏移。因此,相干累加平均得到的圖像邊緣或細(xì)節(jié)會(huì)出現(xiàn)模糊。針對(duì)這一問(wèn)題,我們之前的算法是基于非局域均值算法(Non-Local Means,NLM),利用圖像的局部相似性計(jì)算出圖像之間的局部偏移量,對(duì)圖像進(jìn)行局部偏移校正后再做加權(quán)平均,以達(dá)到提高信噪比的目的。本文在此基礎(chǔ)上提出一種旋轉(zhuǎn)不變的非局域均值方法(Rotation-invariant Non-local Means,RINLM)。該方法采用圓形鄰域區(qū)域,并將其劃分為以中心像素為圓心的一系列等面積的同心圓環(huán),再計(jì)算鄰域模式之間的相似性。與NLM算法相比,本文方法可以利用圖像中發(fā)生相對(duì)旋轉(zhuǎn)的相似鄰域模式,提高算法的去噪性能。將旋轉(zhuǎn)不變的非局域均值算法應(yīng)用于圖像序列的累加和去噪中,本文方法可以克服局部運(yùn)動(dòng)的旋轉(zhuǎn)成分對(duì)計(jì)算的影響,從而更好地處理存在旋轉(zhuǎn)的局部運(yùn)動(dòng)的情況,進(jìn)一步提高圖像質(zhì)量。本文利用模擬數(shù)據(jù)和臨床真實(shí)數(shù)據(jù)進(jìn)行了實(shí)驗(yàn),并采用主觀和客觀的方法對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行了評(píng)價(jià)和分析。結(jié)果顯示,與前人方法相比,本文方法可以進(jìn)一步提高圖像的信噪比,更好的保持圖像邊緣細(xì)節(jié)信息。
[Abstract]:The signal-to-noise ratio (SNR) of magnetic resonance images obtained by low field or fast imaging is often low. In clinical application, increasing the acquisition times and adding the average of multi-frame images are often used to improve the signal-to-noise ratio (SNR) of the images. However, in the process of multiple acquisition, the autonomous or involuntary movement of the human body causes the relative deviation between multiple images. Therefore, the edges or details of the image obtained by the coherent cumulative average will be blurred. In order to solve this problem, our previous algorithm is based on the non-local mean algorithm (Non-Local Means,NLM), using the local similarity of the image to calculate the local offset between images, and then doing the weighted average after the local offset correction of the image. In order to improve the signal-to-noise ratio. In this paper, a rotation-invariant nonlocal mean method (Rotation-invariant Non-local Means,RINLM) is proposed. In this method, the circular neighborhood region is used and divided into a series of concentric rings with the center pixel as the center, and the similarity between the neighborhood patterns is calculated. Compared with the NLM algorithm, the proposed method can improve the denoising performance by using the similar neighborhood pattern of relative rotation in the image. By applying the rotation-invariant nonlocal mean algorithm to the accumulation and denoising of image sequences, the method in this paper can overcome the influence of the rotational component of the local motion on the calculation, and thus better deal with the local motion with rotation. Further improve the image quality. In this paper, simulated data and clinical real data were used to evaluate and analyze the experimental results by subjective and objective methods. The results show that compared with the previous methods, the proposed method can further improve the signal-to-noise ratio (SNR) of the image and keep the edge details of the image better.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

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本文編號(hào)：2279336