利用图像处理技术实现视网膜眼底图像视盘的自动定位与分割

R. GeethaRamani, C. Dhanapackiam
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引用次数: 18

摘要

视盘位置检测与提取是视网膜图像自动分析的重要环节。眼科医生分析视盘以发现视网膜疾病的存在或不存在,如青光眼、糖尿病视网膜病变、闭塞、眶淋巴管瘤、乳头状水肿、垂体癌、开角型青光眼等。本文采用模板匹配方法和形态学方法对眼底图像视盘区域进行定位和分割。视神经发源于视网膜图像中最亮的区域,是利用杯盘比(CDR)和视盘边缘与视盘中心之比检测视网膜病变的主要区域。该方法首先对视盘进行定位和分割,然后确定相应的眼底图像中心点和直径。我们考虑了包含30张视网膜眼底图像的金标准数据库(可在公共存储库中获得)来进行实验。检测视盘的位置,对所有图像进行分割,并根据视盘中心点和直径(由眼科专家指定的基础事实)评估分割后的视盘的中心和直径。通过我们的方法确定的视盘中心和直径接近眼科专家提供的地面事实。与活动轮廓模型、模糊c均值、人工神经网络等视盘检测方法相比,该系统对视盘的定位精度达到98.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic localization and segmentation of Optic Disc in retinal fundus images through image processing techniques
The Optic Disc location detection and extraction are main role of automatically analyzing of retinal image. Ophthalmologists analyze the Optic Disc for finding the presence or absence of retinal diseases viz. Glaucoma, Diabetic Retinopathy, Occlusion, Orbital lymphangioma, Papilloedema, Pituitary Cancer, Open-angle glaucoma etc. In this paper, we attempted to localize and segment the Optic Disc region of retinal fundus images by template matching method and morphological procedure. The optic nerve is originate in the brightest region of retinal image and it act as a main region to detect the retinal diseases using the ratio of cup and disc(CDR) and the ratio between Optic rim & center of the Optic Disc. The proposed work localizes and segments the Optic Disc then the corresponding center points & diameter of retinal fundus images are determined. We have considered the Gold Standard Database (available at public repository) that comprises of 30 retinal fundus images to our experiments. The location of Optic Disc is detected, segmented for all images and the center & diameter of segmented Optic Disc are evaluated against the Optic Disc center points & diameter (ground truth specified by ophthalmologist experts). The Optic Disc centers & diameter identified through our method are near close to ground truth provided by the ophthalmologist experts. The proposed system achieves 98.7% accuracy in locating the Optic Disc while compare with other Optic Disc detection methodologies such as Active Contour Model, Fuzzy C-Means, Artificial Neural Network.
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