用于反射式熒光成像的光譜分離方法
發(fā)布時間：2018-09-14 14:24
【摘要】：反射式熒光成像技術可以連續(xù)、無創(chuàng)、高通量地在體檢測小動物體內被標記的細胞和分子，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，使感興趣熒光團難以準確監(jiān)測和定位。另外，為了同時監(jiān)測多種生物過程，，需要利用多種熒光標記物標記不同的分子進行熒光成像。這些熒光團光譜混疊，無法獨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自發(fā)熒光的去除和多種感興趣熒光團的分離。 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)分不同的目標熒光團。將本算法運用到反射式熒光成像系統(tǒng)中，去除了自發(fā)熒光的影響，實現(xiàn)了分別表達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，在感興趣熒光團的信號較微弱以致于其空間分布無法預測的情況下，本文對上述算法進行了改進。首先用初始化中心點的分類算法對原始多光譜熒光圖像按光譜性質的不同進行分類，獲得感興趣熒光團以及自發(fā)熒光的空間分布后，再提取各個熒光團的純光譜用于線性分離算法。通過在體模型實驗和在體鼻咽癌腫瘤模型實驗進一步驗證了改進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).
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
本文編號：2242965
[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).
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R310
【參考文獻】
相關期刊論文 前5條
1 王榮福;腫瘤核素顯像的臨床應用研究[J];北京醫(yī)學;2004年05期
2 楊闊;張小琴;宋永;秦天鶯;;分子成像技術及應用[J];河南教育學院學報(自然科學版);2010年04期
3 宮彥軍,王艷紅,禹秉熙;高光譜識別目標的光譜分離分析方法[J];內蒙古大學學報(自然科學版);2003年02期
4 莊天戈;;走近分子成像[J];中國醫(yī)療器械雜志;2007年02期
5 種敏琪;秦斌杰;;生物熒光譜分離端元提取算法的實現(xiàn)與比較[J];中國醫(yī)療器械雜志;2010年04期
本文編號：2242965
本文鏈接：http://sikaile.net/yixuelunwen/swyx/2242965.html
