本文選題：馬爾科夫蒙特卡洛 + 數(shù)據(jù)驅(qū)動�。� 參考：《南京航空航天大學(xué)》2011年碩士論文

【摘要】：眼底視網(wǎng)膜血管作為人體非創(chuàng)傷觀察的重要器官,其不同程度的變化能夠反映出高血壓、動脈硬化等心血管疾病的癥狀,特別當(dāng)與血管相關(guān)的器官發(fā)生病變時,眼底血管的直徑、曲率等特征的改變在一定程度上可反映病變的程度。故定量和定性地自動分析視網(wǎng)膜血管具有非常重要的臨床應(yīng)用價值,而視網(wǎng)膜血管分割和提取則是分析血管的首要任務(wù)。 現(xiàn)有多數(shù)視網(wǎng)膜血管分割方法盡管對非病變的血管圖像具有較好的分割效果,但對病變圖像的分割效果仍不理想,尤對光照不均、病灶等敏感。為此,本文提出了一種相對魯棒的血管分割方法。該方法首次嘗試?yán)糜嬎銠C視覺中的Top-down和Bottom-up兩種層次化分割框架相結(jié)合實現(xiàn)視網(wǎng)膜圖像的分割。具體而言,首先在彩色視網(wǎng)膜圖像的綠色通道上利用Curvelet變換進行血管增強。然后在貝葉斯統(tǒng)計框架下,采用可逆跳轉(zhuǎn)的馬爾科夫蒙特卡洛算法搜索參數(shù)空間,從而求得不依賴于初始分割的近似全局最優(yōu)的分割,同時利用數(shù)據(jù)驅(qū)動的均值漂移聚類算法和Canny邊緣檢測算子來加速馬爾科夫鏈的動態(tài)變化。 本文在MATLAB環(huán)境下,采用標(biāo)準(zhǔn)STARE視網(wǎng)膜圖像庫中四幅圖像進行了實驗,實驗結(jié)果表明該方法不僅對非病變的圖像而且對病變的圖像都具有較好的魯棒分割效果,并且通過模式識別中的沒有免費午餐定理在理論上對該方法進行了理論分析。
[Abstract]:The retinal vessels in the fundus of the eye, as an important organ for non-traumatic observation, can reflect the symptoms of cardiovascular diseases such as hypertension, arteriosclerosis and so on, especially when the organs associated with blood vessels are changed. Changes in the diameter and curvature of the fundus vessels can reflect the extent of the lesion to a certain extent. Therefore, quantitative and qualitative automatic analysis of retinal vessels has very important clinical application value, and retinal blood vessel segmentation and extraction is the primary task of vascular analysis. Although most of the existing retinal vascular segmentation methods have good segmentation effect on non-pathological vascular images, the segmentation effect of the diseased images is still not ideal, especially sensitive to uneven illumination and focus. Therefore, a relatively robust blood vessel segmentation method is proposed in this paper. This method is the first attempt to realize retinal image segmentation by combining Top-down and Bottom-up in computer vision. Firstly, the Curvelet transform is used to enhance the blood vessels in the green channel of the color retinal image. Then under the Bayesian statistical framework, the reversible jump Markov Monte Carlo algorithm is used to search the parameter space, and the approximate global optimal segmentation independent of the initial segmentation is obtained. At the same time, the data-driven mean shift clustering algorithm and the Canny edge detection operator are used to accelerate the dynamic change of Markov chain. In this paper, four images in the standard STARE retinal image library are used in MATLAB environment. The experimental results show that the proposed method has a good robust segmentation effect not only for non-pathological images but also for diseased images. The method is theoretically analyzed by the free lunch theorem in pattern recognition.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：R774.1;R318.0;TP391.41

【參考文獻】

相關(guān)期刊論文 前3條

1 姚暢;陳后金;;病變視網(wǎng)膜圖像血管網(wǎng)絡(luò)的自動分割[J];電子學(xué)報;2010年05期

2 王爽,徐亮,李建軍;視網(wǎng)膜微血管異常與心腦血管疾病關(guān)系的流行病學(xué)研究[J];國外醫(yī)學(xué)(眼科學(xué)分冊);2005年03期

3 李居朋;陳后金;張新媛;;基于先驗知識隨機游走模型的視網(wǎng)膜血管分割方法[J];中國生物醫(yī)學(xué)工程學(xué)報;2009年04期

相關(guān)碩士學(xué)位論文 前1條

1 黃勇新;基于眼底影像的計算機輔助診斷研究及系統(tǒng)實現(xiàn)[D];長春理工大學(xué);2007年

，

本文編號：1985128