【摘要】：反射式熒光成像技術(shù)可以連續(xù)、無創(chuàng)、高通量地在體檢測小動物體內(nèi)被標(biāo)記的細(xì)胞和分子，追蹤各種疾病的形成和發(fā)展。然而這種成像方式的缺陷在于在體成像時(shí)皮膚和食物自發(fā)熒光的存在會大大降低系統(tǒng)的探測靈敏度，使感興趣熒光團(tuán)難以準(zhǔn)確監(jiān)測和定位。另外，為了同時(shí)監(jiān)測多種生物過程，，需要利用多種熒光標(biāo)記物標(biāo)記不同的分子進(jìn)行熒光成像。這些熒光團(tuán)光譜混疊，無法獨(dú)立分辨它們各自的信息。而多光譜分離法可用于反射式熒光成像時(shí)自發(fā)熒光的去除和多種感興趣熒光團(tuán)的分離。 本文提出一種多光譜分離方法：從5-6幅多光譜熒光圖像中在體提取自發(fā)熒光和感興趣熒光團(tuán)的純光譜數(shù)據(jù)后，再使用線性分離算法去除自發(fā)熒光，并區(qū)分不同的目標(biāo)熒光團(tuán)。將本算法運(yùn)用到反射式熒光成像系統(tǒng)中，去除了自發(fā)熒光的影響，實(shí)現(xiàn)了分別表達(dá)TagRFP和mLumin熒光蛋白的兩種BL21大腸桿菌樣品的分離。這兩種熒光團(tuán)與自發(fā)熒光的信噪比在分離前后分別從9.23dB和4.70dB提高到35.69dB和24.91dB。此外，在感興趣熒光團(tuán)的信號較微弱以致于其空間分布無法預(yù)測的情況下，本文對上述算法進(jìn)行了改進(jìn)。首先用初始化中心點(diǎn)的分類算法對原始多光譜熒光圖像按光譜性質(zhì)的不同進(jìn)行分類，獲得感興趣熒光團(tuán)以及自發(fā)熒光的空間分布后，再提取各個(gè)熒光團(tuán)的純光譜用于線性分離算法。通過在體模型實(shí)驗(yàn)和在體鼻咽癌腫瘤模型實(shí)驗(yàn)進(jìn)一步驗(yàn)證了改進(jìn)后線性分離算法的可行性。
[Abstract]:The reflective fluorescence imaging technique can continuously, noninvasively and high-throughput detect labeled cells and molecules in small animals and track the formation and development of various diseases. However, the defect of this imaging method is that the presence of skin and food autofluorescence in volume imaging will greatly reduce the detection sensitivity of the system and make it difficult for interested fluorescence groups to accurately monitor and locate. In addition, in order to monitor multiple biological processes simultaneously, a variety of fluorescent markers are used to label different molecules for fluorescence imaging. The spectra of these fluorescence clusters are overlapped and their respective information cannot be identified independently. The multi-spectral separation method can be used for the removal of autofluorescence and the separation of a variety of interesting fluorescence groups in reflective fluorescence imaging. In this paper, a multispectral separation method is proposed: after extracting in vivo the pure spectral data of autofluorescence and interesting fluorescence groups from 5-6 multispectral fluorescence images, the linear separation algorithm is used to remove the autofluorescence and distinguish different target fluorescence groups. The algorithm is applied to the reflective fluorescence imaging system to remove the influence of autofluorescence and to separate two kinds of BL21 Escherichia coli samples expressing TagRFP and mLumin fluorescent proteins respectively. The signal-to-noise ratio of these two groups increased from 9.23dB and 4.70dB to 35.69dB and 24.91 dB before and after separation. In addition, under the condition that the signal of the fluorescence group of interest is weak and its spatial distribution can not be predicted, the above algorithm is improved in this paper. First, the original multispectral fluorescence images are classified according to the spectral properties using the classification algorithm of initializing the center points, and the spatial distribution of the interesting fluorescence groups and the autofluorescence are obtained. Then the pure spectrum of each fluorescence group was extracted for linear separation algorithm. The feasibility of the improved linear separation algorithm is further verified by in vivo model experiments and in vivo tumor model experiments for nasopharyngeal carcinoma (NPC).
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R310

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