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,它能夠引起視盤和視杯形態(tài)的顯著變化。視杯和視盤比值(簡稱杯盤比)的測量在青光眼的檢測中尤為重要。目前較為成熟的青光眼檢測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圖像的杯盤比計(jì)算會更準(zhǔn)確。且在SD-OCT圖像中,杯盤比與視網(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)檢測與杯盤比評估方法,來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圖像相對于由其他方式獲取的圖像的優(yōu)勢,及青光眼病變的臨床表現(xiàn),并通過相關(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)的初步檢測和精確檢測,以及每一步的具體處理流程。初步檢測時(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,但是斷點(diǎn)精度有待提高,故又在精確檢測時(shí)采用了提取樣本、確定特征、合成投影圖像并限定分類的目標(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RPE斷點(diǎn)進(jìn)行識別、利用標(biāo)簽矩陣的梯度信息對異常斷點(diǎn)糾正等操作,實(shí)現(xiàn)了RPE斷點(diǎn)的精確定位,并對斷點(diǎn)的實(shí)驗(yàn)誤差及誤差來源進(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),如盤沿寬度、視網(wǎng)膜神經(jīng)纖維層(retinal nerve fiber layer, RNFL)厚度、杯盤比等,并對杯盤比在青光眼病變診斷中的意義和本文選取杯盤比作為評估標(biāo)準(zhǔn)的合理性進(jìn)行了分析,隨后利用前文檢測出的斷點(diǎn)進(jìn)行了杯盤比計(jì)算和誤差定量分析。最后,對本文算法所完成的工作做出了總結(jié),簡述了本文研究方法的創(chuàng)新點(diǎn)和不足之處,并對下一步需要開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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R775;TP391.41
本文編號：2354743
[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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R775;TP391.41
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