Bump-Based CNV Segmentation Method for SD-OCT Retinal Image

A retinal and concavo-convex technology, applied in the field of CNV automatic segmentation of retinal images based on frequency-domain optical coherence tomography, to overcome blurred or even missing lesion boundaries and the effect of overcoming the influence of CNV segmentation

Active Publication Date: 2019-10-18
NANJING UNIV OF SCI & TECH
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Problems solved by technology

[0003] At present, there are only automatic CNV segmentation methods based on OCT angiography images, and there is no automatic CNV segmentation method based on frequency-domain OCT (SD-OCT), because CNVs in SD-OCT images cannot be distinguished by color differences like OCT angiography images. easier to distinguish

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  • Bump-Based CNV Segmentation Method for SD-OCT Retinal Image

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Embodiment

[0086] The system of the invention takes SD-OCT retinal image as input, and uses image processing means to automatically segment CNV in the input image.

[0087] The flow of this embodiment is as figure 1 As shown, the size of the three-dimensional SD-OCT retina image collected by the OCT imaging device is 1024×512×128, which corresponds to the area of ​​2mm×6mm×6mm of the retina. Figure 7 An original SD-OCT retinal image is given, and several major related tissue structures of the retina are marked in the figure (ILM: inner limiting membrane, RPE: retinal pigment epithelium, BM: Bruch's membrane, CNV: choroidal neoplasia Blood vessels, CSI: chorioscleral boundary). Figure 8 Smooth the result of bilateral filtering, Picture 9 In order to estimate the retina and choroid area based on the reflectance characteristics, since the reflectance of the retina and choroid area is significantly higher than that of other areas such as vitreous and sclera, it can be easily obtained through ...

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Abstract

The invention discloses a spectral-domain optical coherence tomography (SD-OCT) retina image choroidal neovascularization (CNV) segmentation method based on concavity and convexity, and belongs to the technical field of image processing. The method comprises the following steps: first, estimating the retina and choroid area of an input SD-OCT image, and positioning the internal limiting membrane (ILM) and the choroid-sclera junction (CSJ); then, estimating the retina pigment epithelium (RPE) layer according to the gradual change feature of the reflectivity of the retina image, and estimating the Bruch's membrane (BM) layer based on the concavity and convexity of the RPE layer; and finally, estimating a preliminary CNV region according to the thickness difference between the RPE layer and the BM layer, and correcting the upper border of CNV to get a final CNV segmentation result. The experimental results show that the algorithm proposed in the invention can be used to segment CNV robustly and precisely, and is of great significance to facilitating subsequent CNV quantitative analysis and improving the work efficiency of doctors.

Description

Technical field [0001] The invention relates to a CNV automatic segmentation method, in particular to a CNV automatic segmentation method based on a frequency domain optical coherence tomography (SD-OCT) retinal image. Background technique [0002] Choroidal neovascularization (CNV) is one of the main manifestations of advanced age-related macular degeneration (AMD), which can cause subretinal hemorrhage, effusion of subretinal fluid and other diseases that affect vision. Traditional CNV lesion measurement is mainly based on two-dimensional imaging technologies such as fluorescence contrast and indigo green angiography. Optical coherence (OCT) imaging technology can effectively obtain three-dimensional images of the fundus. Based on OCT angiography images, three-dimensional parameters such as the volume of CNV can be measured. So as to analyze CNV more effectively. [0003] At present, there is only an automatic CNV segmentation method based on OCT angiography images. There is no ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/12
Inventor 陈强俞晨琛李鸣超李苹
Owner NANJING UNIV OF SCI & TECH
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