【摘要】：隨著社會(huì)的發(fā)展，人們生活水平的提高，在我國(guó)糖尿病發(fā)病率呈逐年上升趨勢(shì)，糖尿病性視網(wǎng)膜病變是糖尿病的并發(fā)癥之一，也是導(dǎo)致失明或視覺(jué)損傷的主要病因之一，如果能夠得到盡早的診斷和治療，則可以有效的控制病情的發(fā)展。臨床上，眼底圖像是眼科疾病診斷的重要依據(jù)，眼底圖像定期檢查已成為糖尿病視網(wǎng)膜病變的重要篩選手段。作為最早出現(xiàn)和最小能被觀測(cè)的病變對(duì)象，微脈瘤的檢測(cè)和定位對(duì)于早期病變的防治顯得尤為重要。 由于背景紋理及噪聲、視盤(pán)、黃斑、血管等因素的干擾，眼底圖像普遍存在的非均勻照明和非均勻?qū)Ρ榷痊F(xiàn)象，微脈瘤本身的尺寸差異以及因?qū)Ρ榷热醵y以觀測(cè)等因素的影響，相關(guān)病變區(qū)域定位和分割很容易出現(xiàn)錯(cuò)誤，因此微脈瘤的檢測(cè)目前仍是一個(gè)比較困難的任務(wù)。已有文獻(xiàn)報(bào)道中，關(guān)于弱微脈瘤的檢測(cè)準(zhǔn)確率很低，同時(shí)微脈瘤的檢測(cè)易受血管提取的影響，據(jù)觀察有30%的誤否認(rèn)和90%誤確認(rèn)結(jié)果和不準(zhǔn)確、不完整的血管結(jié)構(gòu)提取有關(guān)，因此如何提高正判率同時(shí)降低誤判率仍將研究人員致力解決的問(wèn)題。本文以數(shù)字彩色眼底圖像處理與識(shí)別為基礎(chǔ)，專(zhuān)門(mén)針對(duì)微脈瘤的檢測(cè)做了較深入的研究，致力于提高微脈瘤檢測(cè)的魯棒性和準(zhǔn)確性，主要工作包括以下內(nèi)容： 從背景估計(jì)建模的角度提出一種基于Mahalanobis距離的疑似微脈瘤判別方法，由于血管是造成大量誤確認(rèn)結(jié)果的主要因素，因此為獲得盡可能準(zhǔn)確完整的血管結(jié)構(gòu)以便排除血管像素，我們提出了一種基于Gabor濾波器的非血管結(jié)構(gòu)抑制算子并結(jié)合多尺度和多滯后閾值技術(shù)的精細(xì)血管骨架提取方法，并在后期處理中，我們采用相對(duì)簡(jiǎn)單的形狀分析和雙環(huán)濾波進(jìn)一步去除了虛假病變點(diǎn)。實(shí)驗(yàn)結(jié)果表明，，該方法性能優(yōu)于或逼近于其它同類(lèi)方法，而且能夠極大提高鄰近血管的微脈瘤檢測(cè)精度。 研究了一種結(jié)合多尺度高斯匹配濾波和集成分類(lèi)方法的微脈瘤檢測(cè)方法。由于微脈瘤在空間分布符合二維高斯分布特性，同時(shí)存在較大的尺寸差異變化，通過(guò)多尺度高斯匹配濾波，首先篩選出疑似微脈瘤對(duì)象，并作為種子點(diǎn)利用區(qū)域生長(zhǎng)技術(shù)分割出病變區(qū)域，進(jìn)而提取病變區(qū)域特征信息，最終采用Adaboost神經(jīng)網(wǎng)絡(luò)集成分類(lèi)器檢測(cè)真實(shí)的微脈瘤病變。該方法在公開(kāi)的ROC數(shù)據(jù)集上進(jìn)行了測(cè)試，實(shí)驗(yàn)表明，檢測(cè)性能優(yōu)于以往的雙環(huán)濾波和形態(tài)學(xué)方法。
[Abstract]:With the development of society and the improvement of people's living standard, the incidence of diabetes is increasing year by year in our country. Diabetic retinopathy is one of the complications of diabetes, and it is also one of the main causes of blindness or visual impairment. If you can get early diagnosis and treatment, you can effectively control the development of the disease. In clinic, fundus image is an important basis for the diagnosis of ophthalmic diseases. Regular examination of fundus image has become an important screening method for diabetic retinopathy. As the earliest and least observable lesions, the detection and localization of microvein tumors is particularly important for the prevention and treatment of early lesions. Because of background texture and noise, visual disc, macula, blood vessel and other factors interference, there are non-uniform illumination and non-uniform contrast phenomenon in the fundus image. Because of the difference in the size of microvein tumor itself and the influence of some factors such as weak contrast, the location and segmentation of the related lesions are prone to errors, so the detection of microvein tumor is still a difficult task at present. It has been reported that the detection accuracy of weak microvein tumor is very low, and the detection of microvein tumor is easy to be affected by blood vessel extraction. It is observed that 30% false denial and 90% false confirmation result and inaccurate, incomplete vascular structure extraction are related to the detection of microvein tumor. Therefore, how to improve the positive judgment rate and reduce the false judgment rate will be solved by researchers. Based on the digital color fundus image processing and recognition, this paper makes a deep research on the detection of microvein tumor, and devotes to improving the robustness and accuracy of the detection of microvein tumor. The main work includes the following: from the perspective of background estimation modeling, a method for identifying suspected microvascular tumors based on Mahalanobis distance is proposed, because blood vessels are the main factors causing a large number of false confirmation results. Therefore, in order to obtain as accurate and complete a vascular structure as possible in order to exclude the pixels of the vessel, we propose a method of extracting the fine vascular skeleton based on the Gabor filter and combining the multi-scale and multi-delay threshold techniques. In the post-processing, we further remove the false lesions by using relatively simple shape analysis and double-loop filtering. The experimental results show that the performance of this method is superior to or close to that of other similar methods, and it can greatly improve the detection accuracy of microvein tumors in adjacent blood vessels. A multi-scale Gao Si matched filtering and integrated classification method for microvein tumor detection is studied. Because the spatial distribution of microveinoma accords with the two-dimensional Gao Si distribution and there is a great difference in size, the suspected microveinoma objects are first screened by the multi-scale Gao Si matched filter. As a seed point, the region growing technique is used to segment the lesion area, and then extract the characteristic information of the lesion area. Finally, the Adaboost neural network integrated classifier is used to detect the true microvein tumor lesion. The method is tested on the open ROC dataset. The experimental results show that the detection performance is better than that of the previous dual-loop filtering and morphology methods.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：R587.2;R774.1;TP391.41

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相關(guān)期刊論文 前2條

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