本文選題：2-DE圖像　切入點(diǎn)：重疊蛋白質(zhì)點(diǎn)　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：蛋白質(zhì)組學(xué)在揭示細(xì)胞生命過(guò)程規(guī)律中發(fā)揮越來(lái)越重要的作用。雙向凝膠電泳(2-DE)技術(shù)是一種非常重要的蛋白質(zhì)組學(xué)研究技術(shù)。2-DE圖像分析處理的目標(biāo)是快速識(shí)別單個(gè)蛋白質(zhì)點(diǎn),以及一組凝膠圖像樣本中的表達(dá)差異的蛋白質(zhì)。蛋白點(diǎn)的檢測(cè)是2-DE圖像基于計(jì)算機(jī)處理過(guò)程的基礎(chǔ)環(huán)節(jié),由于2-DE圖像的復(fù)雜性及制作過(guò)程中存在的干擾因素,使得蛋白質(zhì)點(diǎn)的檢測(cè)成為一個(gè)復(fù)雜又耗時(shí)的難題。本論文以2-DE圖像中重疊蛋白質(zhì)點(diǎn)的檢測(cè)為研究重點(diǎn),提出兩種重疊蛋白質(zhì)點(diǎn)的檢測(cè)方法,主要研究?jī)?nèi)容如下:(1)研究了2-DE圖像蛋白質(zhì)點(diǎn)預(yù)檢測(cè)方法。對(duì)比研究了均值濾波,中值濾波和高斯濾波等空間濾波算法,采用高斯濾波算法對(duì)凝膠圖像去噪,降低噪聲對(duì)凝膠圖像后續(xù)蛋白質(zhì)點(diǎn)檢測(cè)的影響;對(duì)比研究了基于閾值、基于分水嶺算法、基于區(qū)域生長(zhǎng)的蛋白質(zhì)點(diǎn)預(yù)分割方法,采用基于區(qū)域生長(zhǎng)算法對(duì)蛋白質(zhì)點(diǎn)進(jìn)行預(yù)檢測(cè),得到清晰的蛋白質(zhì)點(diǎn)邊界。(2)提出了基于凹區(qū)匹配的重疊蛋白質(zhì)點(diǎn)分割方法。首先獲得重疊蛋白質(zhì)點(diǎn)圖像的凸閉包結(jié)構(gòu),并通過(guò)凸閉包與原二值圖的差值獲得重疊蛋白質(zhì)點(diǎn)的凹區(qū);其次根據(jù)重疊蛋白質(zhì)點(diǎn)的重疊模型對(duì)凹區(qū)進(jìn)行兩兩配對(duì);然后根據(jù)匹配的凹區(qū)之間的最短歐式距離來(lái)確定凹點(diǎn);最后通過(guò)連接凹點(diǎn)對(duì)實(shí)現(xiàn)重疊蛋白質(zhì)點(diǎn)的分離。此方法不再?gòu)脑嫉鞍踪|(zhì)點(diǎn)邊界輪廓上提取凹點(diǎn),而是從凹區(qū)的輪廓上提取凹點(diǎn),明顯減小了凹點(diǎn)搜尋的范圍及處理的像素?cái)?shù)量,提高了重疊蛋白質(zhì)點(diǎn)分割的速度。實(shí)驗(yàn)表明,該算法在輕度重疊及重疊復(fù)雜度較小的情況下,分離效果較好。(3)提出了基于角點(diǎn)檢測(cè)與多邊形近似相結(jié)合的重疊蛋白質(zhì)點(diǎn)分割方法。首先采用Harris角點(diǎn)檢測(cè)法得到重疊蛋白質(zhì)點(diǎn)邊界的所有角點(diǎn);其次為降低蛋白質(zhì)點(diǎn)邊界上干擾凹點(diǎn)的影響,將邊界的角點(diǎn)作為特征點(diǎn),利用基于角點(diǎn)的多邊形近似算法對(duì)重疊蛋白質(zhì)點(diǎn)邊界進(jìn)行多邊形近似表示;然后通過(guò)判斷多邊形頂點(diǎn)的凹凸性來(lái)判斷角點(diǎn)的凹凸性進(jìn)而獲得凹角點(diǎn);最后提出凹點(diǎn)匹配準(zhǔn)則,并從凹角點(diǎn)中選取真正的凹點(diǎn)并構(gòu)造分離線,實(shí)現(xiàn)對(duì)重疊蛋白質(zhì)點(diǎn)的精確分割。該算法實(shí)現(xiàn)簡(jiǎn)單,不需要多次腐蝕與膨脹運(yùn)算,最大程度保持蛋白質(zhì)點(diǎn)邊緣。實(shí)驗(yàn)結(jié)果表明,與基于凹區(qū)匹配的算法相比,該算法提取的凹點(diǎn)的誤差更小,對(duì)重疊蛋白質(zhì)點(diǎn)的分離更加準(zhǔn)確,同時(shí)對(duì)重度重疊及重疊復(fù)雜度較大的蛋白質(zhì)點(diǎn)也有顯著的分離效果。
[Abstract]:Proteomics plays a more and more important role in revealing the laws of cell life process. Two-dimensional gel electrophoresis (2-DE) is a very important proteomics research technique. The detection of protein spots in a group of gel image samples is the basic part of 2-DE image processing based on computer. Because of the complexity of 2-DE image and the interference factors in the process of making 2-DE image, The detection of protein spots has become a complicated and time-consuming problem. In this paper, two detection methods of overlapping protein spots in 2-DE images are proposed. The main research contents are as follows: (1) the protein spot pre-detection method of 2-DE image is studied. The spatial filtering algorithms such as mean filter, median filter and Gao Si filter are compared and analyzed. The effect of noise reduction on the detection of protein spots in gel images is studied. The pre-segmentation method of protein points based on threshold, watershed algorithm and region growth is compared, and the pre-detection of protein spots based on region growth algorithm is studied. A new method of overlapping protein point segmentation based on concave matching is proposed. Firstly, the convex closure structure of overlapping protein point image is obtained. The concave region of overlapping protein points is obtained by the difference between convex closure and original binary graph. Secondly, the concave region is pairwise paired according to the overlapping model of overlapping protein points, and the concave point is determined according to the shortest Euclidean distance between the matching concave regions. Finally, the overlapping protein points are separated by connecting concave pairs. Instead of extracting concave points from the boundary contours of the original protein points, the method extracts concave points from the concave contour. The range of concave search and the number of pixels processed are reduced obviously, and the speed of overlapping protein spot segmentation is improved. In this paper, an overlapping protein point segmentation method based on corner detection and polygon approximation is proposed. Firstly, all corners of overlapping protein point boundary are obtained by Harris corner detection method. Secondly, in order to reduce the effect of interference concave on the protein point boundary, the corner of the boundary is taken as the feature point, and the polygonal approximation algorithm based on the corner point is used to represent the overlapping protein point boundary by polygon approximation. Finally, the concave point matching criterion is proposed, and the real concave point is selected from the concave corner and the separation line is constructed. The algorithm is simple to implement, does not require multiple corrosion and expansion operations, and keeps the edge of protein points to the maximum extent. The experimental results show that compared with the algorithm based on concave matching, the proposed algorithm is more efficient than the one based on concave matching. The algorithm has less error in extracting concave points and more accurate separation of overlapped protein spots, and it also has a significant effect on separation of protein spots with heavy overlap and high overlap complexity.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：Q51;TP391.41

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