【摘要】：眼底血管網(wǎng)絡(luò)是人體內(nèi)能夠通過非創(chuàng)直接觀察到的比較深層次的微血管。任何系統(tǒng)性和血液性的病變都會(huì)導(dǎo)致眼底微血管的特征或形態(tài)發(fā)生變化。眼底圖像血管分割是視網(wǎng)膜圖像處理及分析中關(guān)鍵性的步驟,對(duì)系統(tǒng)性和血液性疾病的早期預(yù)防與診斷有較好的研究意義。視網(wǎng)膜圖像的特征較為復(fù)雜,眼底血管的自動(dòng)分割很容易受到外界條件和病變自身的影響,而且在眼底圖像中,微小血管和背景的對(duì)比度低,增加了眼底血管的分割難度,所以提高眼底血管的分割精度是一項(xiàng)重要的研究課題。本文介紹了眼底血管的研究背景和意義及眼球和視網(wǎng)膜的結(jié)構(gòu)特征,闡述了國(guó)內(nèi)外對(duì)眼底圖像處理的研究現(xiàn)狀,分析了血管圖像的性質(zhì)和研究難點(diǎn),重點(diǎn)從眼底圖像去噪和眼底圖像血管分割兩方面進(jìn)行了研究,本文采用DRIVE標(biāo)準(zhǔn)圖像庫(kù)和STARE標(biāo)準(zhǔn)圖像庫(kù)中的彩色眼底圖像進(jìn)行仿真實(shí)驗(yàn),完成的主要研究工作如下:(1)改進(jìn)了一種結(jié)合非局部均值濾波的雙邊濾波眼底圖像去噪方法。對(duì)眼底圖像去噪進(jìn)行了研究,深入分析了雙邊濾波與非局部均值濾波兩種去噪方法,并對(duì)兩種去噪方法的優(yōu)缺點(diǎn)進(jìn)行總結(jié),在這基礎(chǔ)上改進(jìn)一種結(jié)合非局部均值濾波的雙邊濾波眼底圖像去噪方法,與此同時(shí)還采用了積分圖運(yùn)算方法和升余弦函數(shù)近似灰度相似性函數(shù)分別實(shí)現(xiàn)NLMF以及BF的快速運(yùn)算,仿真實(shí)驗(yàn)分析結(jié)果表明改進(jìn)的去噪方法對(duì)眼底圖像的去噪效果較好,運(yùn)算耗時(shí)較少。通過對(duì)眼底血管分割方法的分析與學(xué)習(xí),本文研究了一種基于水平集函數(shù)的彩色眼底血管分割方法。先應(yīng)用自適應(yīng)直方圖和二維Gabor變換對(duì)眼底圖像進(jìn)行預(yù)處理,然后對(duì)眼底圖像運(yùn)用可變區(qū)域擬合能量定義的水平集理論進(jìn)行血管分割仿真實(shí)驗(yàn)。根據(jù)眼底血管特點(diǎn),改進(jìn)一種基于廣義線性模型的彩色眼底圖像眼底血管分割算法。該算法先應(yīng)用自適應(yīng)直方圖均衡法增強(qiáng)視網(wǎng)膜圖像;然后,采用不同尺度的二維Gabor小波對(duì)眼底圖像進(jìn)行變換;最后,采用廣義線性模型(generalized linear model,GLM)分類器對(duì)眼底圖像進(jìn)行血管分割。
[Abstract]:Fundus vascular network is a deeper microvessel that can be observed directly by non-invasive method. Any systemic and hematologic lesions can lead to changes in the characteristics or morphology of the fundus microvessels. Ocular fundus image vascular segmentation is a key step in retinal image processing and analysis. It is of great significance for early prevention and diagnosis of systemic and hematologic diseases. The characteristics of retinal images are complex, and the automatic segmentation of fundus vessels is easy to be affected by the external conditions and the pathological changes themselves. Moreover, in the fundus images, the contrast between the tiny vessels and the background is low, which increases the difficulty of the segmentation of the fundus vessels. Therefore, improving the segmentation accuracy of ocular fundus vessels is an important research topic. This paper introduces the background and significance of the study of fundus vessels and the structural characteristics of the eyeball and retina, expounds the present situation of the research on the image processing of the fundus at home and abroad, and analyzes the properties and difficulties of the image of the blood vessel. In this paper, the image denoising of fundus and the segmentation of blood vessel in fundus image are studied. In this paper, the color fundus image in DRIVE standard image database and STARE standard image database are used for simulation experiment. The main works are as follows: (1) an improved bilateral filtering method for denoising fundus images with non-local mean filtering is proposed. In this paper, the denoising of fundus image is studied, and the two denoising methods of bilateral filtering and non-local mean filtering are deeply analyzed, and the advantages and disadvantages of the two methods are summarized. On the basis of this, an improved two-sided filtering method for denoising the fundus images with non-local mean filtering is improved. At the same time, the integral image operation method and the raised cosine function approximate gray similarity function are used to realize the fast operation of NLMF and BF, respectively. The simulation results show that the improved de-noising method has a good effect on the image denoising of the fundus, and the computation time is less. Based on the analysis and study of the fundus blood vessel segmentation method, a color fundus blood vessel segmentation method based on the level set function is studied in this paper. At first, adaptive histogram and two-dimensional Gabor transform are used to preprocess the fundus image, and then the level set theory of variable region fitting energy is used to simulate the blood vessel segmentation of the fundus image. According to the characteristics of fundus vessels, a color fundus image segmentation algorithm based on generalized linear model is improved. The algorithm firstly uses adaptive histogram equalization method to enhance retinal image, then uses two-dimensional Gabor wavelet to transform the fundus image. Finally, the generalized linear model (generalized linear model-GLM) classifier is used to segment the blood vessel of the fundus image.
【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R770.4;TP391.41

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