本文關(guān)鍵詞： 眼底圖像 視覺特征 血管輔助 Gbvs C-V模型　出處：《天津工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：彩色眼底圖像的處理與分析不僅可以用于診斷某些眼科疾病,還可以幫助醫(yī)生進(jìn)行糖尿病、高血壓等全身性疾病的診斷,跟蹤病情的發(fā)展。在眼底視網(wǎng)膜圖像中,視盤是一個(gè)類圓形的近黃色或白色的亮斑,同時(shí)是眼底血管的發(fā)源地,匯聚著大量較粗的血管,其形狀、大小和深度等參數(shù)是衡量眼底健康狀況的重要指標(biāo)。準(zhǔn)確的視盤檢測不僅可以輔助定位血管、黃斑等重要的眼底組織結(jié)構(gòu),還可輔助確定滲出物、微動脈瘤等病變的位置,對眼底圖像分析具有重要的意義。本文方法充分利用視盤的亮度、對比度、相位一致性三類視覺特征以及主血管結(jié)構(gòu)特性,提出一種結(jié)合改進(jìn)型基于圖的顯著性模型(Gbvs)和眼底血管結(jié)構(gòu)特征的視盤檢測方法。該方法首先對Gbvs模型進(jìn)行改進(jìn),將其中的顏色、亮度、方向三類特征改為亮度、對比度、結(jié)合相位一致性(PC)三類特征,并利用改進(jìn)后的Gbvs模型構(gòu)造眼底圖像的顯著圖;然后提靜脈血管輪廓線,進(jìn)行拋物線擬合,通過比較拋物線頂點(diǎn)鄰域內(nèi)的顯著性與整幅眼底圖像的平均顯著性的大小確定視盤位置;最后,消除視盤局部區(qū)域的血管,利用C-V水平集方法來確定視盤邊界,獲得視盤分割結(jié)果。在四個(gè)公開的眼底圖像數(shù)據(jù)集(DRIVE、MESSIDOR、STARE和DIABETEDO)上對該方法進(jìn)行了測試,平均定位準(zhǔn)確率分別為100%、99.25%、90.12%、96.1%,高于現(xiàn)有代表性方法。
[Abstract]:Color fundus image processing and analysis can not only be used to diagnose some eye diseases, but also to help doctors to diagnose diabetes, hypertension and other systemic diseases, tracking the development of the disease. The optic disc is a round, nearly yellow or white bright spot, and is the origin of the fundus vessels, which gather a large number of thicker blood vessels, the shape of which, The parameters such as size and depth are important indexes to measure the fundus health. Accurate optical disk detection can not only assist in locating the blood vessels, macula and other important ocular fundus tissue structure, but also help to determine the location of exudates, microaneurysms and other lesions. This method makes full use of three visual features, such as brightness, contrast, phase consistency, and main vascular structure. In this paper, a new method of visual disk detection based on improved graph-based saliency model (Gbvs) and fundus vascular features is proposed. Firstly, the Gbvs model is improved to change the color, brightness and directional features to luminance and contrast. Combining with three kinds of features, the improved Gbvs model was used to construct the salient image of the fundus, and then the contour of the levator vein was fitted with parabola. The position of the disc is determined by comparing the significance in the parabola vertex neighborhood with the average significance of the whole fundus image. Finally, the blood vessels in the local area of the disc are eliminated, and the boundary of the disc is determined by using the C-V level set method. The results of visual disk segmentation were obtained. The method was tested on four open fundus image data sets, namely, DRIVE / MESSIDORSTARE and DIABETEDO. the average accuracy of the method was 100 and 99.250.12.1, which was higher than that of the existing representative methods.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R770.4;TP391.41

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1 張亞男;結(jié)合改進(jìn)型GBVS模型和眼底血管結(jié)構(gòu)特征的視盤檢測方法研究[D];天津工業(yè)大學(xué);2017年

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本文編號：1512295