基于OCT圖像的青光眼病變定量分析研究
發(fā)布時(shí)間：2018-11-24 19:19
【摘要】：青光眼是一種致盲性特別高的眼部疾病,它能夠引起視盤(pán)和視杯形態(tài)的顯著變化。視杯和視盤(pán)比值(簡(jiǎn)稱(chēng)杯盤(pán)比)的測(cè)量在青光眼的檢測(cè)中尤為重要。目前較為成熟的青光眼檢測(cè)方法大都是基于彩色眼底圖像的,然而,頻譜光學(xué)相干層析圖像(spectral domain optical coherence tomography, SD-OCT)較眼底圖像具有更高的精確度和可靠性,所以,基于SD-OCT圖像的杯盤(pán)比計(jì)算會(huì)更準(zhǔn)確。且在SD-OCT圖像中,杯盤(pán)比與視網(wǎng)膜色素上皮層(retinal pigment epithelium, RPE)斷點(diǎn)密切相關(guān),因此,本文的研究目的是提出一種基于SD-OCT圖像的視網(wǎng)膜色素上皮層斷點(diǎn)檢測(cè)與杯盤(pán)比評(píng)估方法,來(lái)輔助青光眼病變的定量分析。首先,介紹了文章使用的實(shí)驗(yàn)數(shù)據(jù)SD-OCT圖像相對(duì)于由其他方式獲取的圖像的優(yōu)勢(shì),及青光眼病變的臨床表現(xiàn),并通過(guò)相關(guān)文獻(xiàn),介紹了目前青光眼發(fā)病部位視神經(jīng)頭區(qū)域在眼底圖像和SD-OCT圖像中的研究現(xiàn)狀；接著,闡述了青光眼研究的重要性和本課題的研究意義。然后,給出了本文方法的具體描述,包括斷點(diǎn)的初步檢測(cè)和精確檢測(cè),以及每一步的具體處理流程。初步檢測(cè)時(shí)采用了閾值分割和二值形態(tài)學(xué)、圖像拉平的相關(guān)知識(shí),但是斷點(diǎn)精度有待提高,故又在精確檢測(cè)時(shí)采用了提取樣本、確定特征、合成投影圖像并限定分類(lèi)的目標(biāo)區(qū)域、分別利用支持向量機(jī)(support vector machine, SVM)和極限學(xué)習(xí)機(jī)(extreme learning machine, ELM)對(duì)RPE斷點(diǎn)進(jìn)行識(shí)別、利用標(biāo)簽矩陣的梯度信息對(duì)異常斷點(diǎn)糾正等操作,實(shí)現(xiàn)了RPE斷點(diǎn)的精確定位,并對(duì)斷點(diǎn)的實(shí)驗(yàn)誤差及誤差來(lái)源進(jìn)行了相關(guān)分析。接著,介紹了一些青光眼病變的診斷標(biāo)準(zhǔn)及可以輔助診斷的相關(guān)指標(biāo),如盤(pán)沿寬度、視網(wǎng)膜神經(jīng)纖維層(retinal nerve fiber layer, RNFL)厚度、杯盤(pán)比等,并對(duì)杯盤(pán)比在青光眼病變?cè)\斷中的意義和本文選取杯盤(pán)比作為評(píng)估標(biāo)準(zhǔn)的合理性進(jìn)行了分析,隨后利用前文檢測(cè)出的斷點(diǎn)進(jìn)行了杯盤(pán)比計(jì)算和誤差定量分析。最后,對(duì)本文算法所完成的工作做出了總結(jié),簡(jiǎn)述了本文研究方法的創(chuàng)新點(diǎn)和不足之處,并對(duì)下一步需要開(kāi)展的工作給出了一些展望和建議。
[Abstract]:Glaucoma is a particularly blind eye disease that can cause significant changes in the shape of the optic disc and cup. The measurement of the ratio of cup to disc is particularly important in the detection of glaucoma. At present, the more mature methods of glaucoma detection are mostly based on color fundus images. However, the spectral optical coherence tomography (spectral domain optical coherence tomography, SD-OCT) has higher accuracy and reliability than the fundus images. Cups and disks based on SD-OCT images are more accurate than calculations. In SD-OCT images, the ratio of cup to disc is closely related to the breakpoint of (retinal pigment epithelium, RPE) in the retinal pigment epithelium layer. The purpose of this paper is to propose a method to detect the breakpoint of retinal pigment epithelium and evaluate the cup / disc ratio based on SD-OCT image to assist the quantitative analysis of glaucoma. First of all, the advantages of the experimental data SD-OCT images compared with those obtained by other methods, and the clinical manifestations of glaucoma are introduced. The present research status of optic nerve head area in the fundus and SD-OCT images of glaucoma is introduced. Then, the importance of glaucoma research and the significance of this research are expounded. Then, the detailed description of the method is given, including the preliminary detection and accurate detection of breakpoints, and the specific processing flow of each step. Threshold segmentation, binary morphology and image leveling are used in the initial detection, but the accuracy of breakpoints needs to be improved. The projection image is synthesized and the target area is defined. The support vector machine (support vector machine, SVM) and the extreme learning machine (extreme learning machine, ELM) are used to identify the breakpoints of RPE, and the gradient information of label matrix is used to correct the abnormal breakpoints. The accurate location of RPE breakpoint is realized, and the experimental error and error source of breakpoint are analyzed. Then, the diagnostic criteria of some glaucoma lesions and the relevant indexes that can be used to assist the diagnosis are introduced, such as the width of disc edge, the thickness of retinal nerve fiber layer (retinal nerve fiber layer, RNFL), the ratio of cup to disc, etc. The significance of cup / disc ratio in the diagnosis of glaucoma and the reasonableness of selecting cup / disc ratio as the evaluation criterion are analyzed, and then the cup / disc ratio and error quantitative analysis are carried out by using the breakpoints detected above. Finally, the work of the algorithm is summarized, the innovations and shortcomings of the research methods are briefly described, and some prospects and suggestions are given for the next work to be carried out.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：R775;TP391.41
本文編號(hào)：2354742
[Abstract]:Glaucoma is a particularly blind eye disease that can cause significant changes in the shape of the optic disc and cup. The measurement of the ratio of cup to disc is particularly important in the detection of glaucoma. At present, the more mature methods of glaucoma detection are mostly based on color fundus images. However, the spectral optical coherence tomography (spectral domain optical coherence tomography, SD-OCT) has higher accuracy and reliability than the fundus images. Cups and disks based on SD-OCT images are more accurate than calculations. In SD-OCT images, the ratio of cup to disc is closely related to the breakpoint of (retinal pigment epithelium, RPE) in the retinal pigment epithelium layer. The purpose of this paper is to propose a method to detect the breakpoint of retinal pigment epithelium and evaluate the cup / disc ratio based on SD-OCT image to assist the quantitative analysis of glaucoma. First of all, the advantages of the experimental data SD-OCT images compared with those obtained by other methods, and the clinical manifestations of glaucoma are introduced. The present research status of optic nerve head area in the fundus and SD-OCT images of glaucoma is introduced. Then, the importance of glaucoma research and the significance of this research are expounded. Then, the detailed description of the method is given, including the preliminary detection and accurate detection of breakpoints, and the specific processing flow of each step. Threshold segmentation, binary morphology and image leveling are used in the initial detection, but the accuracy of breakpoints needs to be improved. The projection image is synthesized and the target area is defined. The support vector machine (support vector machine, SVM) and the extreme learning machine (extreme learning machine, ELM) are used to identify the breakpoints of RPE, and the gradient information of label matrix is used to correct the abnormal breakpoints. The accurate location of RPE breakpoint is realized, and the experimental error and error source of breakpoint are analyzed. Then, the diagnostic criteria of some glaucoma lesions and the relevant indexes that can be used to assist the diagnosis are introduced, such as the width of disc edge, the thickness of retinal nerve fiber layer (retinal nerve fiber layer, RNFL), the ratio of cup to disc, etc. The significance of cup / disc ratio in the diagnosis of glaucoma and the reasonableness of selecting cup / disc ratio as the evaluation criterion are analyzed, and then the cup / disc ratio and error quantitative analysis are carried out by using the breakpoints detected above. Finally, the work of the algorithm is summarized, the innovations and shortcomings of the research methods are briefly described, and some prospects and suggestions are given for the next work to be carried out.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：R775;TP391.41
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