【摘要】：20世紀(jì)70年代以來，一系列新的成像技術(shù)的出現(xiàn)使得神經(jīng)科學(xué)家和醫(yī)生們可以有效地觀察到腦的功能性活動，為解釋腦的奧秘提供了有效方法。其中基于內(nèi)源信號的腦功能光學(xué)成像是一種前沿的腦成像技術(shù)，它因空間分辨率較高，可長時間在體記錄，結(jié)構(gòu)簡單等特點，可以有效地研究大腦功能特性，在腦功能研究中發(fā)揮了重要作用。雖然基于內(nèi)源信號的腦功能光學(xué)成像的原理和系統(tǒng)構(gòu)成比較簡單，但在活體成像實驗過程中存在諸如呼吸、心跳以及血管周期性波動等生物噪聲，其引起的皮層反射光強變化往往遠(yuǎn)高于刺激相關(guān)的皮層活動信號，這些生物噪聲降低了成像信噪比，需要經(jīng)過一系列圖像信號處理之后才能從噪聲中提取到所需要的真正反應(yīng)大腦功能的信息，因而研究適用于光學(xué)成像的圖像處理方法成為了研究熱點。早期光學(xué)成像研究中使用的疊加平均及差分等的方法可以有效地從噪聲中提取信號，提高信噪比，但在某些情況下，噪聲仍然較強，存在的血管偽跡也降低了圖像質(zhì)量，因而工程技術(shù)人員一直在致力于研究和建立適于內(nèi)源信號腦光學(xué)成像的圖像處理新方法。 在本課題組進(jìn)行的內(nèi)源信號腦光學(xué)成像實驗中，得到的圖像信號較強但存在血管偽跡較強的問題，降低了信噪比。因此本文旨在探索多種血管偽跡提取及去除方法，然后從中尋找到最適合于本課題組實際實驗結(jié)果的血管偽跡提取及去除方法。本文首先介紹了課題中主要應(yīng)用的血管偽跡提取方法：方差法和局部相似度最小化方法，兩者都可以取得對血管偽跡較準(zhǔn)確的預(yù)測，但是方差法存在著對于局部細(xì)節(jié)預(yù)測不夠準(zhǔn)確并且有預(yù)測錯誤的情況；而局部相似度最小化方法則不能對血管偽跡的本身形態(tài)、長寬粗細(xì)等給予很好的表達(dá)，因此本文在這里創(chuàng)新性地把兩種方法結(jié)合使用，得到了比單獨使用兩種方法更好的血管偽跡預(yù)測效果。其次本文系統(tǒng)研究了血管偽跡去除的算法，提出用血管偽跡周圍灰度值中位值去除的方法，可以有效的對血管偽跡進(jìn)行去除，進(jìn)一步比較了不同參數(shù)對該算法的影響，并進(jìn)行了性能分析。本文在血管偽跡提取去除算法的基礎(chǔ)上，創(chuàng)新性地把該方法與主成分分析、獨立成分分析相結(jié)合，證明了把主成分分析和獨立成分分析作為數(shù)據(jù)預(yù)處理手段，再使用血管偽跡提取及去除方法，可以很大程度上改善內(nèi)源信號腦光學(xué)成像圖像質(zhì)量，比單獨使用主成分分析和獨立成分分析，能夠取得更好的效果。 本文的主要結(jié)論是：把方差法和局部相似度最小化法相結(jié)合可以預(yù)測出較好的血管偽跡，在此基礎(chǔ)上用中位值去除血管偽跡能得到較好的偽跡處理效果。PCA和ICA作為圖像預(yù)處理手段，結(jié)合上述偽跡檢測和去除手段，能進(jìn)一步增強圖像信噪比，提高圖像質(zhì)量。
[Abstract]:Since the 1970s, a series of new imaging techniques have enabled neuroscientists and doctors to effectively observe the functional activities of the brain and provide an effective way to explain the mysteries of the brain. Among them, brain functional optical imaging based on endogenous signal is a kind of advanced brain imaging technology. Because of its high spatial resolution, long time in vivo recording, simple structure and so on, it can effectively study the functional characteristics of the brain. It plays an important role in the study of brain function. Although the principle and system of brain functional optical imaging based on endogenous signals are relatively simple, biological noises such as breathing, heartbeat and periodic fluctuations of blood vessels exist in the experimental process of in-vivo imaging. The cortical reflectance intensity changes are often much higher than the cortical activity signals associated with stimulation, and these biological noises reduce the imaging signal-to-noise ratio (SNR). It takes a series of image signal processing to extract the real information of brain function from noise, so the research of image processing method suitable for optical imaging has become a hot topic. The superposition averaging and differential methods used in the early optical imaging research can effectively extract signals from noise and improve SNR. However, in some cases, the noise is still strong, and the existing vascular artifacts also reduce the image quality. Therefore, engineers have been working to study and establish a new image processing method suitable for endogenous signal brain optical imaging. In the experiment of brain optical imaging with endogenous signal, the result shows that the image signal is strong but the vascular artifact is strong, which reduces the signal-to-noise ratio (SNR). Therefore, the purpose of this paper is to explore a variety of methods for vascular artifact extraction and removal, and then to find out the most suitable methods for vascular artifact extraction and removal. This paper first introduces the main methods of vascular artifact extraction: variance method and local similarity minimization method, both of which can obtain accurate prediction of vascular artifact. However, the variance method can not predict the local details accurately and has the wrong prediction, while the local similarity minimization method can not give a good expression of the shape of the vascular artifacts, length, width, thickness, etc. Therefore, this paper innovatively combines the two methods to obtain a better prediction effect of vascular artifacts than using the two methods alone. Secondly, the algorithm of vascular artifact removal is systematically studied in this paper, and the method of removing the median value of the gray value around the vascular artifact is put forward, which can effectively remove the vascular artifact, and the influence of different parameters on the algorithm is further compared. The performance analysis is also carried out. On the basis of vascular artifact extraction and removal algorithm, this paper innovatively combines this method with principal component analysis and independent component analysis, and proves that principal component analysis and independent component analysis are used as data preprocessing methods. The method of vascular artifact extraction and removal can improve the image quality of endogenous signal brain optical imaging to a great extent, which is better than that of principal component analysis and independent component analysis alone. The main conclusion of this paper is that the combination of variance method and local similarity minimization method can predict better vascular artifacts. On this basis, using median value to remove vascular artifact can get better artifact processing effect. PCA and ICA can be used as image preprocessing means. Combining with the above artifact detection and removal methods, image SNR can be further enhanced and image quality can be improved.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.0

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本文編號：2258320